- published: 28 Aug 2013
- views: 6161
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Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY
- published: 29 Dec 2008
- views: 41495
This video is the first in a series of videos related to the basics of power analyses.
All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/
- published: 07 Jul 2015
- views: 613
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParris@gmail.com indicating your university affiliation and which videos you've found useful.
Thank you! - Dr. Julian Parris
----
Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests.
- published: 25 Oct 2011
- views: 32639
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
- published: 13 Mar 2013
- views: 103279
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
- published: 15 Apr 2014
- views: 48477
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example.
A "one sided, less than" example can be found here:
http://www.youtube.com/watch?v=zXbSogwX8Wc
Stoney Pryor
- published: 13 Jun 2011
- views: 58848
Goal: Define statistical power and sample size in clinical practice and the USMLE boards
Objectives:
Identify the factors affecting statistical power
Differentiate between type I and II errors
Select methods to improve statistical power in research studies
Differentiate between sensitivity, specificity, positive predictive value and negative predictive value
Understand a receiver operator curve
- published: 28 Sep 2012
- views: 29487
Statistical power
The power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true. It can be equivalently thought of as the probability of accepting the alternative hypothesis (H1) when it is true—that is, the ability of a test to detect an effect, if the effect actually exists. That is,
The power of a test sometimes, less formally, refers to the probability of rejecting the null when it is not correct, though this is not the formal definition stated above. The power is in general a function of the possible distributions, often determined by a parameter, under the alternative hypothesis. As the power increases, there are decreasing chances of a Type II error (false negative), which are also referred to as the false negative rate (β) since the power is equal to 1−β, again, under the alternative hypothesis. A similar concept is Type I error, also referred to as the “false positive rate” or the level of a test under the null hypothesis.
This page contains text from Wikipedia, the Free Encyclopedia -
https://wn.com/Statistical_power
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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17:28Statistical Power
Statistical Power -
12:07Statistical power #1
Statistical power #1Statistical power #1
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3:59What is Statistical Power?
What is Statistical Power?What is Statistical Power?
Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY -
3:15What is Statistical Power?
What is Statistical Power? -
8:22What is statistical power
What is statistical powerWhat is statistical power
This video is the first in a series of videos related to the basics of power analyses. All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/ -
29:19Calculating Statistical Power Tutorial
Calculating Statistical Power TutorialCalculating Statistical Power Tutorial
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParris@gmail.com indicating your university affiliation and which videos you've found useful. Thank you! - Dr. Julian Parris ---- Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests. -
8:11Type I Errors, Type II Errors, and the Power of the Test
Type I Errors, Type II Errors, and the Power of the TestType I Errors, Type II Errors, and the Power of the Test
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test. -
9:42Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative ResearchType I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention. This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship. For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?" The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out." Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout." The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary. In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables. Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis. A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one. A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it. To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred. A type II error happens when you decide your prediction is wrong when you are actually right. One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules. It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study. When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level. Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists. The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ... -
12:13Calculating Power
Calculating PowerCalculating Power
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example. A "one sided, less than" example can be found here: http://www.youtube.com/watch?v=zXbSogwX8Wc Stoney Pryor -
37:00Power and sample size
Power and sample sizePower and sample size
Goal: Define statistical power and sample size in clinical practice and the USMLE boards Objectives: Identify the factors affecting statistical power Differentiate between type I and II errors Select methods to improve statistical power in research studies Differentiate between sensitivity, specificity, positive predictive value and negative predictive value Understand a receiver operator curve
-
-
Statistical power #1
published: 03 Oct 2013 -
What is Statistical Power?
Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY
published: 29 Dec 2008 -
-
What is statistical power
This video is the first in a series of videos related to the basics of power analyses. All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/
published: 07 Jul 2015 -
Calculating Statistical Power Tutorial
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParris@gmail.com indicating your university affiliation and which videos you've found useful. Thank you! - Dr. Julian Parris ---- Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests.
published: 25 Oct 2011 -
Type I Errors, Type II Errors, and the Power of the Test
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
published: 13 Mar 2013 -
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention. This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers...
published: 15 Apr 2014 -
Calculating Power
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example. A "one sided, less than" example can be found here: http://www.youtube.com/watch?v=zXbSogwX8Wc Stoney Pryor
published: 13 Jun 2011 -
Power and sample size
Goal: Define statistical power and sample size in clinical practice and the USMLE boards Objectives: Identify the factors affecting statistical power Differentiate between type I and II errors Select methods to improve statistical power in research studies Differentiate between sensitivity, specificity, positive predictive value and negative predictive value Understand a receiver operator curve
published: 28 Sep 2012
Statistical Power
- Order: Reorder
- Duration: 17:28
- Updated: 28 Aug 2013
- views: 6161
http://thedoctoraljourney.com/ This tutorial focuses on the power of a statistical procedure and how power is maximized.
For more statistics, research and SPS...
http://thedoctoraljourney.com/ This tutorial focuses on the power of a statistical procedure and how power is maximized.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
wn.com/Statistical Power
Statistical power #1
- Order: Reorder
- Duration: 12:07
- Updated: 03 Oct 2013
- views: 7891
- published: 03 Oct 2013
- views: 7891
What is Statistical Power?
- Order: Reorder
- Duration: 3:59
- Updated: 29 Dec 2008
- views: 41495
Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY
Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY
wn.com/What Is Statistical Power
Definition of power, Type II errors, and sample size issues. More info at my website: http://bit.ly/5fuFsY
- published: 29 Dec 2008
- views: 41495
What is Statistical Power?
- Order: Reorder
- Duration: 3:15
- Updated: 14 Feb 2013
- views: 7198
Illustration of how statistical power works, and how it can increase or decrease.
Related blog post: http://www.andysbrainblog.blogspot.com/2013/02/the-will-to...
Illustration of how statistical power works, and how it can increase or decrease.
Related blog post: http://www.andysbrainblog.blogspot.com/2013/02/the-will-to-fmri-power.html
wn.com/What Is Statistical Power
What is statistical power
- Order: Reorder
- Duration: 8:22
- Updated: 07 Jul 2015
- views: 613
This video is the first in a series of videos related to the basics of power analyses.
All materials shown in the video, as well as content from the other vide...
This video is the first in a series of videos related to the basics of power analyses.
All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/
wn.com/What Is Statistical Power
This video is the first in a series of videos related to the basics of power analyses.
All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/
- published: 07 Jul 2015
- views: 613
Calculating Statistical Power Tutorial
- Order: Reorder
- Duration: 29:19
- Updated: 25 Oct 2011
- views: 32639
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParri...
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParris@gmail.com indicating your university affiliation and which videos you've found useful.
Thank you! - Dr. Julian Parris
----
Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests.
wn.com/Calculating Statistical Power Tutorial
If you are at a university other than UCSD and have found this or any of my other videos to be useful, please do me a favor and send me a note at ProfessorParris@gmail.com indicating your university affiliation and which videos you've found useful.
Thank you! - Dr. Julian Parris
----
Tutorial on Visualizing and Calculating Statistical Power for simple hypothesis testing using z-tests.
- published: 25 Oct 2011
- views: 32639
Type I Errors, Type II Errors, and the Power of the Test
- Order: Reorder
- Duration: 8:11
- Updated: 13 Mar 2013
- views: 103279
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
wn.com/Type I Errors, Type Ii Errors, And The Power Of The Test
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and beta), and the power of a hypothesis test.
- published: 13 Mar 2013
- views: 103279
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
- Order: Reorder
- Duration: 9:42
- Updated: 15 Apr 2014
- views: 48477
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology re...
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
wn.com/Type I And Ii Errors, Power, Effect Size, Significance And Power Analysis In Quantitative Research
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
- published: 15 Apr 2014
- views: 48477
Calculating Power
- Order: Reorder
- Duration: 12:13
- Updated: 13 Jun 2011
- views: 58848
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given inf...
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example.
A "one sided, less than" example can be found here:
http://www.youtube.com/watch?v=zXbSogwX8Wc
Stoney Pryor
wn.com/Calculating Power
How to calculate beta and power. This video attempts to simply explain the concept of statistical power. The first half of the video works with some given information (Ho/Ha, n, sigma, and alpha). At the 8 minute mark, I introduce the alternative mu of 20.5 (a hypothetical value, as are most alternative values of mu, to calculate the power of the test against this alternative). This is a "two sided, greater than" example.
A "one sided, less than" example can be found here:
http://www.youtube.com/watch?v=zXbSogwX8Wc
Stoney Pryor
- published: 13 Jun 2011
- views: 58848
Power and sample size
- Order: Reorder
- Duration: 37:00
- Updated: 28 Sep 2012
- views: 29487
Goal: Define statistical power and sample size in clinical practice and the USMLE boards
Objectives:
Identify the factors affecting statistical power
Differenti...
Goal: Define statistical power and sample size in clinical practice and the USMLE boards
Objectives:
Identify the factors affecting statistical power
Differentiate between type I and II errors
Select methods to improve statistical power in research studies
Differentiate between sensitivity, specificity, positive predictive value and negative predictive value
Understand a receiver operator curve
wn.com/Power And Sample Size
Goal: Define statistical power and sample size in clinical practice and the USMLE boards
Objectives:
Identify the factors affecting statistical power
Differentiate between type I and II errors
Select methods to improve statistical power in research studies
Differentiate between sensitivity, specificity, positive predictive value and negative predictive value
Understand a receiver operator curve
- published: 28 Sep 2012
- views: 29487
-
Read Statistical Analysis Ebook PDF
More Info http://j.mp/2fgfBuD Use Excel 2013’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2013 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including recently introduced consistency functions. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, this edition ...
published: 10 Nov 2016 -
Audiobook: Statistical Power Analysis for the Behavioral Sciences
Get your free audiobook or ebook: http://yazz.space/sabk/35/en/B01DY7LRM0/info Statistical Power Analysis for the Behavioral Sciences, Revised Edition emphasizes the importance of statistical power analysis. This edition discusses the concepts and types of power analysis, t test for means, significance of a product moment rs, and differences between correlation coefficients. The test that a proportion is .50 and sign test, differences between proportions, and chi-square tests for goodness of fit and contingency tables are also elaborated. This text likewise covers the F tests of variance proportions in multiple regression/correlation analysis and computational procedures. This publication is intended for behavioral and biosocial scientists who use statistical inference, but also serves as ...
published: 28 Oct 2016 -
Introduction to Statistical Power (Pagano Chapter 11 of Understanding Statistics)
This is an introduction to the concept of power in statistics. This is important to help you choose an appropriate sample size. This is part of a lecture series (5 videos) covering several supplemental topics in statistics. The PowerPoint and data sets can be downloaded at: https://www.researchgate.net/profile/David_Dunaetz/publication/308996129_PowerPoint_for_Supplemental_Topics_in_Statistics/links/57fd7a5508ae49db47553c1b If you have not installed the Data Analysis Toolpak (which comes free with Excel), the following video will show you how to do it. Windows: https://www.youtube.com/watch?v=rq8VynGNAFU Mac: https://www.youtube.com/watch?v=1R_aJ_Fli2w
published: 10 Oct 2016 -
18 - Power calculation for Ttest type data inR
This lecture shows how to calculate the power for Ttest type data using R
published: 10 Oct 2016 -
17 - Power calculation for Ttest-data
This lecture covers how to calculate the power for data which are going to be evaluated with the t-test
published: 10 Oct 2016 -
Statistical Power Analysis for the Social and Behavioral Sciences: Basic and Advanced E-Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B00GISRRLS/book This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, Sas, and Spss. Readers can verify the power...
published: 06 Oct 2016 -
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach E-Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B007NYFOUC/book Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in th...
published: 03 Oct 2016 -
Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
2014 Fall Meeting Section: Hydrology Session: Advances in Hydrometeorological Predictions and Applications I Title: Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events Authors: DeChant, C M, Civil and Environmental Engineering, Portland State University, Portland, OR, United States Moradkhani, H, Civil and Environmental Engineering, Portland State University, Portland, OR, United States Abstract: http://abstractsearch.agu.org/meetings/2014/FM/H34A-03
published: 30 Sep 2016 -
Statistical Power Creep in Force of Will / Card Chat
Force of Will has had a lot of complaints about Power Creep over it's four block life, but that talk is mostly about effects. That Statistics of the card have Power Crept too. The card I mention as 500/500 but 400/400 normally is The Servant of the Mikage.
published: 29 Sep 2016 -
Statistical Power Analysis: A Simple and General Model for Traditional and Modern E-Book
Read your free e-book: http://copydl.space/mebk/50/en/B00M6T8R1W/book Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the F statistic and reviews how to determine: the sample size needed to achie...
published: 29 Sep 2016
Read Statistical Analysis Ebook PDF
- Order: Reorder
- Duration: 3:15
- Updated: 10 Nov 2016
- views: 0
More Info http://j.mp/2fgfBuD
Use Excel 2013’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2013 to perform co...
More Info http://j.mp/2fgfBuD
Use Excel 2013’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2013 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including recently introduced consistency functions. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, this edition adds two more chapters on inferential statistics, covering crucial topics ranging from experimental design to the statistical power of F tests. Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-by-step projects—all complemented by extensive web-based resources. Master Excel’s most useful descriptive and inferential statistical tools Tell the truth with statistics—and recognize when others don’t Accurately summarize sets of values Infer a population’s characteristics from a sample’s frequency distribution Explore correlation and regression to learn how variables move in tandem Use Excel consistency functions such as STDEV.S() and STDEV.P() Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in Use ANOVA to test differences between more than two means Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2013 shortcuts
wn.com/Read Statistical Analysis Ebook Pdf
More Info http://j.mp/2fgfBuD
Use Excel 2013’s statistical tools to transform your data into knowledge Conrad Carlberg shows how to use Excel 2013 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including recently introduced consistency functions. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, this edition adds two more chapters on inferential statistics, covering crucial topics ranging from experimental design to the statistical power of F tests. Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-by-step projects—all complemented by extensive web-based resources. Master Excel’s most useful descriptive and inferential statistical tools Tell the truth with statistics—and recognize when others don’t Accurately summarize sets of values Infer a population’s characteristics from a sample’s frequency distribution Explore correlation and regression to learn how variables move in tandem Use Excel consistency functions such as STDEV.S() and STDEV.P() Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in Use ANOVA to test differences between more than two means Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2013 shortcuts
- published: 10 Nov 2016
- views: 0
Audiobook: Statistical Power Analysis for the Behavioral Sciences
- Order: Reorder
- Duration: 1:33
- Updated: 28 Oct 2016
- views: 0
Get your free audiobook or ebook: http://yazz.space/sabk/35/en/B01DY7LRM0/info
Statistical Power Analysis for the Behavioral Sciences, Revised Edition emphasize...
Get your free audiobook or ebook: http://yazz.space/sabk/35/en/B01DY7LRM0/info
Statistical Power Analysis for the Behavioral Sciences, Revised Edition emphasizes the importance of statistical power analysis. This edition discusses the concepts and types of power analysis, t test for means, significance of a product moment rs, and differences between correlation coefficients. The test that a proportion is .50 and sign test, differences between proportions, and chi-square tests for goodness of fit and contingency tables are also elaborated. This text likewise covers the F tests of variance proportions in multiple regression/correlation analysis and computational procedures. This publication is intended for behavioral and biosocial scientists who use statistical inference, but also serves as a supplementary textbook for intermediate level courses in applied statistics in behavioral/biosocial science.
wn.com/Audiobook Statistical Power Analysis For The Behavioral Sciences
Get your free audiobook or ebook: http://yazz.space/sabk/35/en/B01DY7LRM0/info
Statistical Power Analysis for the Behavioral Sciences, Revised Edition emphasizes the importance of statistical power analysis. This edition discusses the concepts and types of power analysis, t test for means, significance of a product moment rs, and differences between correlation coefficients. The test that a proportion is .50 and sign test, differences between proportions, and chi-square tests for goodness of fit and contingency tables are also elaborated. This text likewise covers the F tests of variance proportions in multiple regression/correlation analysis and computational procedures. This publication is intended for behavioral and biosocial scientists who use statistical inference, but also serves as a supplementary textbook for intermediate level courses in applied statistics in behavioral/biosocial science.
- published: 28 Oct 2016
- views: 0
Introduction to Statistical Power (Pagano Chapter 11 of Understanding Statistics)
- Order: Reorder
- Duration: 10:07
- Updated: 10 Oct 2016
- views: 8
This is an introduction to the concept of power in statistics. This is important to help you choose an appropriate sample size.
This is part of a lecture serie...
This is an introduction to the concept of power in statistics. This is important to help you choose an appropriate sample size.
This is part of a lecture series (5 videos) covering several supplemental topics in statistics.
The PowerPoint and data sets can be downloaded at:
https://www.researchgate.net/profile/David_Dunaetz/publication/308996129_PowerPoint_for_Supplemental_Topics_in_Statistics/links/57fd7a5508ae49db47553c1b
If you have not installed the Data Analysis Toolpak (which comes free with Excel), the following video will show you how to do it.
Windows: https://www.youtube.com/watch?v=rq8VynGNAFU
Mac: https://www.youtube.com/watch?v=1R_aJ_Fli2w
wn.com/Introduction To Statistical Power (Pagano Chapter 11 Of Understanding Statistics)
This is an introduction to the concept of power in statistics. This is important to help you choose an appropriate sample size.
This is part of a lecture series (5 videos) covering several supplemental topics in statistics.
The PowerPoint and data sets can be downloaded at:
https://www.researchgate.net/profile/David_Dunaetz/publication/308996129_PowerPoint_for_Supplemental_Topics_in_Statistics/links/57fd7a5508ae49db47553c1b
If you have not installed the Data Analysis Toolpak (which comes free with Excel), the following video will show you how to do it.
Windows: https://www.youtube.com/watch?v=rq8VynGNAFU
Mac: https://www.youtube.com/watch?v=1R_aJ_Fli2w
- published: 10 Oct 2016
- views: 8
18 - Power calculation for Ttest type data inR
- Order: Reorder
- Duration: 9:25
- Updated: 10 Oct 2016
- views: 33
This lecture shows how to calculate the power for Ttest type data using R
This lecture shows how to calculate the power for Ttest type data using R
wn.com/18 Power Calculation For Ttest Type Data Inr
This lecture shows how to calculate the power for Ttest type data using R
- published: 10 Oct 2016
- views: 33
17 - Power calculation for Ttest-data
- Order: Reorder
- Duration: 15:58
- Updated: 10 Oct 2016
- views: 18
This lecture covers how to calculate the power for data which are going to be evaluated with the t-test
This lecture covers how to calculate the power for data which are going to be evaluated with the t-test
wn.com/17 Power Calculation For Ttest Data
This lecture covers how to calculate the power for data which are going to be evaluated with the t-test
- published: 10 Oct 2016
- views: 18
Statistical Power Analysis for the Social and Behavioral Sciences: Basic and Advanced E-Book
- Order: Reorder
- Duration: 3:26
- Updated: 06 Oct 2016
- views: 2
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B00GISRRLS/book
This is the first book to demonstrate the application of power analysis to the newer m...
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B00GISRRLS/book
This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, Sas, and Spss. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.this book will help readers do just that. Sample computer code in R, Spss, and Sas at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal.organized according to various techniques, chapters 1 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 6 cover common statistical proceduresanalysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 11 review the new statistical proceduresmulti-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (hlm) and/or multilevel modeling and Sem taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the books unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.
wn.com/Statistical Power Analysis For The Social And Behavioral Sciences Basic And Advanced E Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B00GISRRLS/book
This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, Sas, and Spss. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.this book will help readers do just that. Sample computer code in R, Spss, and Sas at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal.organized according to various techniques, chapters 1 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 6 cover common statistical proceduresanalysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 11 review the new statistical proceduresmulti-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (hlm) and/or multilevel modeling and Sem taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the books unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.
- published: 06 Oct 2016
- views: 2
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach E-Book
- Order: Reorder
- Duration: 3:08
- Updated: 03 Oct 2016
- views: 3
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B007NYFOUC/book
Statistical power analysis has revolutionized the ways in which we conduct and evaluat...
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B007NYFOUC/book
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test ones ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the books application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (sas, Spss, Stata, Lisrel, Amos, and Mplus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics Ii, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The books applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.
wn.com/Statistical Power Analysis With Missing Data A Structural Equation Modeling Approach E Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B007NYFOUC/book
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test ones ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the books application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (sas, Spss, Stata, Lisrel, Amos, and Mplus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics Ii, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The books applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.
- published: 03 Oct 2016
- views: 3
Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
- Order: Reorder
- Duration: 22:19
- Updated: 30 Sep 2016
- views: 0
2014 Fall Meeting
Section: Hydrology
Session: Advances in Hydrometeorological Predictions and Applications I
Title: Maximizing Statistical Power When Verifyi...
2014 Fall Meeting
Section: Hydrology
Session: Advances in Hydrometeorological Predictions and Applications I
Title: Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
Authors:
DeChant, C M, Civil and Environmental Engineering, Portland State University, Portland, OR, United States
Moradkhani, H, Civil and Environmental Engineering, Portland State University, Portland, OR, United States
Abstract:
http://abstractsearch.agu.org/meetings/2014/FM/H34A-03
wn.com/Maximizing Statistical Power When Verifying Probabilistic Forecasts Of Hydrometeorological Events
2014 Fall Meeting
Section: Hydrology
Session: Advances in Hydrometeorological Predictions and Applications I
Title: Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
Authors:
DeChant, C M, Civil and Environmental Engineering, Portland State University, Portland, OR, United States
Moradkhani, H, Civil and Environmental Engineering, Portland State University, Portland, OR, United States
Abstract:
http://abstractsearch.agu.org/meetings/2014/FM/H34A-03
- published: 30 Sep 2016
- views: 0
Statistical Power Creep in Force of Will / Card Chat
- Order: Reorder
- Duration: 7:15
- Updated: 29 Sep 2016
- views: 236
Force of Will has had a lot of complaints about Power Creep over it's four block life, but that talk is mostly about effects. That Statistics of the card have P...
Force of Will has had a lot of complaints about Power Creep over it's four block life, but that talk is mostly about effects. That Statistics of the card have Power Crept too.
The card I mention as 500/500 but 400/400 normally is The Servant of the Mikage.
wn.com/Statistical Power Creep In Force Of Will Card Chat
Force of Will has had a lot of complaints about Power Creep over it's four block life, but that talk is mostly about effects. That Statistics of the card have Power Crept too.
The card I mention as 500/500 but 400/400 normally is The Servant of the Mikage.
- published: 29 Sep 2016
- views: 236
Statistical Power Analysis: A Simple and General Model for Traditional and Modern E-Book
- Order: Reorder
- Duration: 2:52
- Updated: 29 Sep 2016
- views: 0
Read your free e-book: http://copydl.space/mebk/50/en/B00M6T8R1W/book
Noted for its accessible approach, this text applies the latest approaches of power analys...
Read your free e-book: http://copydl.space/mebk/50/en/B00M6T8R1W/book
Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the F statistic and reviews how to determine: the sample size needed to achieve desired levels of power; the level of power needed in a study; the size of effect that can be reliably detected by a study; and sensible criteria for statistical significance. The book helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out out the way they do.the fourth edition features:-new Boxed Material sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs.-many more worked examples help readers apply the concepts presented.-expanded coverage of power analysis for multifactor analysis of variance (anova) to show readers how to analyze up to four factors with repeated measures on any or all of the factors.-re-designed and expanded web based One Stop F Calculator software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests.-easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies.intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics Ii, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the books applied approach.
wn.com/Statistical Power Analysis A Simple And General Model For Traditional And Modern E Book
Read your free e-book: http://copydl.space/mebk/50/en/B00M6T8R1W/book
Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the F statistic and reviews how to determine: the sample size needed to achieve desired levels of power; the level of power needed in a study; the size of effect that can be reliably detected by a study; and sensible criteria for statistical significance. The book helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out out the way they do.the fourth edition features:-new Boxed Material sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs.-many more worked examples help readers apply the concepts presented.-expanded coverage of power analysis for multifactor analysis of variance (anova) to show readers how to analyze up to four factors with repeated measures on any or all of the factors.-re-designed and expanded web based One Stop F Calculator software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests.-easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies.intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics Ii, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the books applied approach.
- published: 29 Sep 2016
- views: 0
-
Understanding Statistical Power Example 1
Statistical Power is a conditional probability of a correct decision given that there truly is an effect. Power is affected by the sample size, the effect size, the alpha, and the variance.
published: 26 Sep 2013 -
Statistical Power and Effect Size Part 1
Statistics for the Behavioral Sciences
published: 19 Apr 2011 -
Statistical Power and Effect Size Part 2
Statistics for the Behavioral Sciences
published: 19 Apr 2011 -
iteachstats 2014 - Lecture 4 - Multiple Comparisons and Statistical Power
An introduction to the multiple comparison problem (i.e. multiple null hypotheses) and how to deal with these using Bonferroni's correction and Tukey's Post-Hoc test. SPSS is also used to run tests multiple times (akin to repeating an experiment) to demonstrate that the p-value itself has a distribution. This is used as the basis for introducing the concept of statistical power.
published: 27 Oct 2014 -
Understanding Statistical Power for Non-Statisticians | 6-28-16
Dale W. Usner, Ph.D., President at SDC, explains the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.
published: 29 Jun 2016 -
How to Use SPSS: Estimating Appropriate Sample Size
Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sample size. G*Power Download site: http:--www.psycho.uni-duesseldorf.de-abteilungen-aap-gpower3-download-and-register Web-Based Calculators: http:--danielsoper.com-statcalc3-default.aspx (scroll down to menu labelled -Sample Size-
published: 09 Sep 2013 -
-
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research by Peter Homel, PhD, Department of Pain Medicine and Palliative Care, Beth Israel Medical Center, New York, NY Lecture presented on 12/12/2011
published: 30 Dec 2011
Understanding Statistical Power Example 1
- Order: Reorder
- Duration: 22:10
- Updated: 26 Sep 2013
- views: 2870
Statistical Power is a conditional probability of a correct decision given that there truly is an effect. Power is affected by the sample size, the effect size,...
Statistical Power is a conditional probability of a correct decision given that there truly is an effect. Power is affected by the sample size, the effect size, the alpha, and the variance.
wn.com/Understanding Statistical Power Example 1
Statistical Power is a conditional probability of a correct decision given that there truly is an effect. Power is affected by the sample size, the effect size, the alpha, and the variance.
- published: 26 Sep 2013
- views: 2870
Statistical Power and Effect Size Part 1
- Order: Reorder
- Duration: 67:36
- Updated: 19 Apr 2011
- views: 6009
Statistics for the Behavioral Sciences
Statistics for the Behavioral Sciences
wn.com/Statistical Power And Effect Size Part 1
Statistical Power and Effect Size Part 2
- Order: Reorder
- Duration: 62:48
- Updated: 19 Apr 2011
- views: 2711
Statistics for the Behavioral Sciences
Statistics for the Behavioral Sciences
wn.com/Statistical Power And Effect Size Part 2
iteachstats 2014 - Lecture 4 - Multiple Comparisons and Statistical Power
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- Duration: 156:02
- Updated: 27 Oct 2014
- views: 322
An introduction to the multiple comparison problem (i.e. multiple null hypotheses) and how to deal with these using Bonferroni's correction and Tukey's Post-Hoc...
An introduction to the multiple comparison problem (i.e. multiple null hypotheses) and how to deal with these using Bonferroni's correction and Tukey's Post-Hoc test. SPSS is also used to run tests multiple times (akin to repeating an experiment) to demonstrate that the p-value itself has a distribution. This is used as the basis for introducing the concept of statistical power.
wn.com/Iteachstats 2014 Lecture 4 Multiple Comparisons And Statistical Power
An introduction to the multiple comparison problem (i.e. multiple null hypotheses) and how to deal with these using Bonferroni's correction and Tukey's Post-Hoc test. SPSS is also used to run tests multiple times (akin to repeating an experiment) to demonstrate that the p-value itself has a distribution. This is used as the basis for introducing the concept of statistical power.
- published: 27 Oct 2014
- views: 322
Understanding Statistical Power for Non-Statisticians | 6-28-16
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- Duration: 61:10
- Updated: 29 Jun 2016
- views: 242
Dale W. Usner, Ph.D., President at SDC, explains the basics of statistical power for non-statisticians, highlighting what you need to know about statistical pow...
Dale W. Usner, Ph.D., President at SDC, explains the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.
wn.com/Understanding Statistical Power For Non Statisticians | 6 28 16
How to Use SPSS: Estimating Appropriate Sample Size
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- Duration: 32:35
- Updated: 09 Sep 2013
- views: 50764
Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sample size.
G*Power Download site: http:--www.psycho.uni-duesseldorf.de-a...
Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sample size.
G*Power Download site: http:--www.psycho.uni-duesseldorf.de-abteilungen-aap-gpower3-download-and-register
Web-Based Calculators: http:--danielsoper.com-statcalc3-default.aspx
(scroll down to menu labelled -Sample Size-
wn.com/How To Use Spss Estimating Appropriate Sample Size
Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sample size.
G*Power Download site: http:--www.psycho.uni-duesseldorf.de-abteilungen-aap-gpower3-download-and-register
Web-Based Calculators: http:--danielsoper.com-statcalc3-default.aspx
(scroll down to menu labelled -Sample Size-
- published: 09 Sep 2013
- views: 50764
Basic Statistical concepts to determine the minimum sample size and statistical power...
- Order: Reorder
- Duration: 86:47
- Updated: 24 Oct 2013
- views: 367
Dr. Erick Suárez
Seminar
Basic Statistical concepts to determine the minimum sample size and statistical power to assess statistical hypotheses
Dr. Erick Suárez
Seminar
Basic Statistical concepts to determine the minimum sample size and statistical power to assess statistical hypotheses
wn.com/Basic Statistical Concepts To Determine The Minimum Sample Size And Statistical Power...
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research
- Order: Reorder
- Duration: 49:27
- Updated: 30 Dec 2011
- views: 5758
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research by Peter Homel, PhD, Department of Pain Medicine and Palliative Care, Beth Israel Medi...
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research by Peter Homel, PhD, Department of Pain Medicine and Palliative Care, Beth Israel Medical Center, New York, NY
Lecture presented on 12/12/2011
wn.com/Hypothesis Testing, Power, And Sample Size Estimation In Medical Research
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research by Peter Homel, PhD, Department of Pain Medicine and Palliative Care, Beth Israel Medical Center, New York, NY
Lecture presented on 12/12/2011
- published: 30 Dec 2011
- views: 5758
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17:28
Statistical Power
http://thedoctoraljourney.com/ This tutorial focuses on the power of a statistical proced...
published: 28 Aug 2013
Statistical Power
Statistical Power
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12:07
Statistical power #1
published: 03 Oct 2013
Statistical power #1
Statistical power #1
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3:59
What is Statistical Power?
Definition of power, Type II errors, and sample size issues. More info at my website: http...
published: 29 Dec 2008
What is Statistical Power?
What is Statistical Power?
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3:15
What is Statistical Power?
Illustration of how statistical power works, and how it can increase or decrease.
Related...
published: 14 Feb 2013
What is Statistical Power?
What is Statistical Power?
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8:22
What is statistical power
This video is the first in a series of videos related to the basics of power analyses.
Al...
published: 07 Jul 2015
What is statistical power
What is statistical power
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29:19
Calculating Statistical Power Tutorial
If you are at a university other than UCSD and have found this or any of my other videos t...
published: 25 Oct 2011
Calculating Statistical Power Tutorial
Calculating Statistical Power Tutorial
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8:11
Type I Errors, Type II Errors, and the Power of the Test
A discussion of Type I errors, Type II errors, their probabilities of occurring (alpha and...
published: 13 Mar 2013
Type I Errors, Type II Errors, and the Power of the Test
Type I Errors, Type II Errors, and the Power of the Test
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9:42
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for ...
published: 15 Apr 2014
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
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12:13
Calculating Power
How to calculate beta and power. This video attempts to simply explain the concept of sta...
published: 13 Jun 2011
Calculating Power
Calculating Power
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37:00
Power and sample size
Goal: Define statistical power and sample size in clinical practice and the USMLE boards
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published: 28 Sep 2012
Power and sample size
Power and sample size
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3:15
Read Statistical Analysis Ebook PDF
More Info http://j.mp/2fgfBuD
Use Excel 2013’s statistical tools to transform your data in...
published: 10 Nov 2016
Read Statistical Analysis Ebook PDF
Read Statistical Analysis Ebook PDF
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1:33
Audiobook: Statistical Power Analysis for the Behavioral Sciences
Get your free audiobook or ebook: http://yazz.space/sabk/35/en/B01DY7LRM0/info
Statistical...
published: 28 Oct 2016
Audiobook: Statistical Power Analysis for the Behavioral Sciences
Audiobook: Statistical Power Analysis for the Behavioral Sciences
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10:07
Introduction to Statistical Power (Pagano Chapter 11 of Understanding Statistics)
This is an introduction to the concept of power in statistics. This is important to help y...
published: 10 Oct 2016
Introduction to Statistical Power (Pagano Chapter 11 of Understanding Statistics)
Introduction to Statistical Power (Pagano Chapter 11 of Understanding Statistics)
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9:25
18 - Power calculation for Ttest type data inR
This lecture shows how to calculate the power for Ttest type data using R
published: 10 Oct 2016
18 - Power calculation for Ttest type data inR
18 - Power calculation for Ttest type data inR
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15:58
17 - Power calculation for Ttest-data
This lecture covers how to calculate the power for data which are going to be evaluated wi...
published: 10 Oct 2016
17 - Power calculation for Ttest-data
17 - Power calculation for Ttest-data
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3:26
Statistical Power Analysis for the Social and Behavioral Sciences: Basic and Advanced E-Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B00GISRRLS/book
This is the firs...
published: 06 Oct 2016
Statistical Power Analysis for the Social and Behavioral Sciences: Basic and Advanced E-Book
Statistical Power Analysis for the Social and Behavioral Sciences: Basic and Advanced E-Book
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3:08
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach E-Book
Read your free e-book: http://hotaudiobook.com/mebk/50/en/B007NYFOUC/book
Statistical powe...
published: 03 Oct 2016
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach E-Book
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach E-Book
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22:19
Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
2014 Fall Meeting
Section: Hydrology
Session: Advances in Hydrometeorological Prediction...
published: 30 Sep 2016
Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events
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7:15
Statistical Power Creep in Force of Will / Card Chat
Force of Will has had a lot of complaints about Power Creep over it's four block life, but...
published: 29 Sep 2016
Statistical Power Creep in Force of Will / Card Chat
Statistical Power Creep in Force of Will / Card Chat
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2:52
Statistical Power Analysis: A Simple and General Model for Traditional and Modern E-Book
Read your free e-book: http://copydl.space/mebk/50/en/B00M6T8R1W/book
Noted for its access...
published: 29 Sep 2016
Statistical Power Analysis: A Simple and General Model for Traditional and Modern E-Book
Statistical Power Analysis: A Simple and General Model for Traditional and Modern E-Book
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22:10
Understanding Statistical Power Example 1
Statistical Power is a conditional probability of a correct decision given that there trul...
published: 26 Sep 2013
Understanding Statistical Power Example 1
Understanding Statistical Power Example 1
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67:36
Statistical Power and Effect Size Part 1
Statistics for the Behavioral Sciences
published: 19 Apr 2011
Statistical Power and Effect Size Part 1
Statistical Power and Effect Size Part 1
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62:48
Statistical Power and Effect Size Part 2
Statistics for the Behavioral Sciences
published: 19 Apr 2011
Statistical Power and Effect Size Part 2
Statistical Power and Effect Size Part 2
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156:02
iteachstats 2014 - Lecture 4 - Multiple Comparisons and Statistical Power
An introduction to the multiple comparison problem (i.e. multiple null hypotheses) and how...
published: 27 Oct 2014
iteachstats 2014 - Lecture 4 - Multiple Comparisons and Statistical Power
iteachstats 2014 - Lecture 4 - Multiple Comparisons and Statistical Power
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- published: 27 Oct 2014
- views: 322
61:10
Understanding Statistical Power for Non-Statisticians | 6-28-16
Dale W. Usner, Ph.D., President at SDC, explains the basics of statistical power for non-s...
published: 29 Jun 2016
Understanding Statistical Power for Non-Statisticians | 6-28-16
Understanding Statistical Power for Non-Statisticians | 6-28-16
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- published: 29 Jun 2016
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32:35
How to Use SPSS: Estimating Appropriate Sample Size
Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sampl...
published: 09 Sep 2013
How to Use SPSS: Estimating Appropriate Sample Size
How to Use SPSS: Estimating Appropriate Sample Size
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86:47
Basic Statistical concepts to determine the minimum sample size and statistical power...
Dr. Erick Suárez
Seminar
Basic Statistical concepts to determine the minimum sample siz...
published: 24 Oct 2013
Basic Statistical concepts to determine the minimum sample size and statistical power...
Basic Statistical concepts to determine the minimum sample size and statistical power...
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- published: 24 Oct 2013
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49:27
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research by Peter Homel, ...
published: 30 Dec 2011
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research
Hypothesis Testing, Power, and Sample Size Estimation in Medical Research
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Understanding Statistical Power Example 1...
Statistical Power and Effect Size Part 1...
Statistical Power and Effect Size Part 2...
iteachstats 2014 - Lecture 4 - Multiple Comparison...
Understanding Statistical Power for Non-Statistici...
How to Use SPSS: Estimating Appropriate Sample Siz...
Basic Statistical concepts to determine the minimu...
Hypothesis Testing, Power, and Sample Size Estimat...
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Clinton Was Reportedly Inconsolable After Election Loss
Edit Opposing Views 12 Nov 2016
Hillary Clinton was reportedly inconsolable after the announcement that Donald Trump had beat her in the presidential election. Clinton “couldn’t stop crying,” conservative author Ed Klein said a source told him November 9 on Newsmax TV’s “The Steve Malzberg Show.”. "About 6.30 [the] morning [of November 9] she called an old friend," he said. "She was crying, inconsolably.” ... Trump is unfit to serve ... ....
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'Impossible' space engine may actually work: NASA
Edit The Times of India 13 Nov 2016
HOUSTON. A controversial and puzzling engine design that could potentially make space travel much cheaper and faster may actually work, a new NASA study suggests. The experimental propulsion system known as the EmDrive, which seems to violate the laws of physics, generated small amounts of thrust in a lab test, researchers said ... Click here to download it for your device....
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Tsunami hits New Zealand after massive quake
Edit Yahoo Daily News 13 Nov 2016
By Charlotte Greenfield ... Geological Survey said, causing widespread damage and generating a tsunami ... "The whole house rolled like a serpent and some things smashed, the power went out," a woman, who gave her name as Elizabeth, told Radio New Zealand from her home in Takaka, near the top of the South Island ... Several roads around the South Island had been closed and damage to power and phone networks was reported ... ....
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Iraqi troops capture Nimrud, site of ancient Assyrian city, from Isis
Edit The Guardian 13 Nov 2016
Iraq says city close to archaeological site bulldozed by Islamic State militants two years ago has been liberated. Iraqi soldiers have captured Nimrud, the site of an ancient Assyrian city overrun by Islamic State militants two years ago, according to the military. “Troops from the Ninth Armoured Division liberated Nimrud town completely and raised the Iraqi flag above its buildings,” the statement said on Sunday ... Read more ... ....
Agenda
Edit Kathimerini 13 Nov 2016
The Hellenic Statistical Authority (ELSTAT) will issue its August data on construction activity and its September statistics on industrial import prices ... The Hellenic Statistical Authority (ELSTAT) will release the September readings of its farming and agriculture import and export indices ... The Hellenic Statistical Authority (ELSTAT) will issue its ......
HB domestic violence figures concerning
Edit NZ Herald 13 Nov 2016
In terms of domestic violence across the country the statistics are not good.The statistics which encompass crime and injury associated with incidents of domestic violence clearly show it is a significant problem in New Zealand... ....
Small business and start-up failure rate: is it a cause for concern?
Edit Sydney Morning Herald 13 Nov 2016
There appears to be a missing link between what the economists are telling us about the strength of the economy and what statistics are saying in terms of the rate of business failures – and the faltering life of new businesses....
5 things to know before today’s Falcons at Eagles’ kickoff
Edit Atlanta Journal 13 Nov 2016
The Atlanta Journal-Constitution’s D ... Atlanta Falcons quarterback Matt Ryan (2) calls to his team in the second half of an NFL football game against the Seattle Seahawks, Sunday, Oct ... 1 ... Here’s how the Falcons and the Eagles stack up statistically.. 2016 Regular Season Statistics Rankings. Falcons Eagles ... Coleman amassed 163 yards to power the offense ... AJC POWER RANKINGS ... Tight ends help to power offense ... AJC’S NFL POWER RANKINGS....
Trump Country: Democratic Elliott County, KY, by 23 points
Edit Springfield News-Sun 13 Nov 2016
Despite vexing McConnell with its "resistance," the county by definition has as its advocate the most powerful man in the chamber ... But what residents of the county's hollows want from those soon to be in power is rooted in its coal-infused past ... The Big Sandy power plant in Louisa, which once propped up the eastern Kentucky economy with its massive coal purchases, started using natural gas....
Trump Country: What one county tells us about the election
Edit WPTV 13 Nov 2016
Despite vexing McConnell with its "resistance," the county by definition has as its advocate the most powerful man in the Senate ... But what residents of the county's hollows want from those soon to be in power is rooted in its coal-infused past ... The Big Sandy power plant in Louisa, which once propped up the eastern Kentucky economy with its massive coal purchases, started using natural gas....
2017 Ford F-150 Online Orders and Customization Now Available, Truck Ready to Enter Production
Edit Master Herald 13 Nov 2016
Ford have even released some statistics on the interest in their truck from region to region, and unsurprisingly, Texans are among the most interested in the new F-150 ... It’s highly likely that more than half of those configurations were just someone’s daydream and not an actual intention to buy, but we won’t know for sure until we’ve seen the actual sales statistics for the truck. Page 1 of 212». ....
Japan's laundromat bubble shows no sign of bursting
Edit Topix 13 Nov 2016
According to the Ministry of Internal Affairs and Communications, as of 2013, 34.6 percent of all Japanese households owned washing machines with some sort of drying function. For the most part, these were drum-type appliances that both washed and dried, and thus the statistic did not take into account households with separate stand-alone clothes dryers ... ....
Missouri-Alabama A&M; basketball lineups, TV, tipoff time
Edit Kansas City Star 13 Nov 2016
Alabama A&M; at Missouri WHEN/WHERE. 7 p.m. Sunday at Mizzou Arena in Columbia TV/RADIO. SEC Network; KMBZ (98.1 FM) Projected starters (2015-16 season statistics for Missouri) P No. Alabama A&M; … Click to Continue » ... ....
Tourism creates 1.3 million jobs for Saudis
Edit Arabnews 13 Nov 2016
RIYADH. Statistical and survey studies conducted by the Tourism Information and Research Center of the Saudi Commission for Tourism and National Heritage (SCTNH) showed that the tourism sector is one of the largest job-generating economic sectors. It ranks second in terms of providing jobs to Saudis after the banking sector, according to a report in Aleqtisadiah, a sister publication of Arab News. Main category. . Saudi Arabia ... ....
Lower build costs are key to lower rents
Edit Irish Independent 13 Nov 2016
Here are five worrying statistics ......
GUEST COLUMN: Millennials and mountains are our future
Edit Colorado Springs Gazette 13 Nov 2016
The city is beautiful. The climate is great. And the outdoor leisure and recreation opportunities are boundless ... Statistics show that's not necessarily the case ... ....
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