Statistics is the study of the collection, organization, analysis, and interpretation of data.^[1][2] It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments.^[1]

A statistician is someone who is particularly well versed in the ways of thinking necessary for the successful application of statistical analysis. Such people have often gained this experience through working in any of a wide number of fields. There is also a discipline called mathematical statistics that studies statistics mathematically.

The word statistics, when referring to the scientific discipline, is singular, as in "Statistics is an art."^[3] This should not be confused with the word statistic, referring to a quantity (such as mean or median) calculated from a set of data,^[4] whose plural is statistics ("this statistic seems wrong" or "these statistics are misleading").

More probability density will be found the closer one gets to the expected (mean) value in a normal distribution. Statistics used in standardized testing assessment are shown. The scales include standard deviations, cumulative percentages, percentile equivalents, Z-scores, T-scores, standard nines, and percentages in standard nines.

Scope[link]

Some consider statistics to be a mathematical body of science pertaining to the collection, analysis, interpretation or explanation, and presentation of data,^[5] while others consider it a branch of mathematics^[6] concerned with collecting and interpreting data. Because of its empirical roots and its focus on applications, statistics is usually considered to be a distinct mathematical science rather than a branch of mathematics.^[7][8] Much of statistics is non-mathematical: ensuring that data collection is undertaken in a way that allows valid conclusions to be drawn; coding and archiving of data so that information is retained and made useful for international comparisons of official statistics; reporting of results and summarised data (tables and graphs) in ways that are comprehensible to those who need to make use of them; implementing procedures that ensure the privacy of census information.

Statisticians improve the quality of data with the design of experiments and survey sampling. Statistics also provides tools for prediction and forecasting using data and statistical models. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Statistical consultants are available to provide help for organizations and companies without direct access to expertise relevant to their particular problems.

Statistical methods can be used for summarizing or describing a collection of data; this is called descriptive statistics. This is useful in research, when communicating the results of experiments. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and are then used for drawing inferences about the process or population being studied; this is called inferential statistics. Inference is a vital element of scientific advance, since it provides a means for drawing conclusions from data that are subject to random variation. To prove the propositions being investigated further, the conclusions are tested as well, as part of the scientific method. Descriptive statistics and analysis of the new data tend to provide more information as to the truth of the proposition.

Descriptive statistics and the application of inferential statistics (a.k.a., predictive statistics) together comprise applied statistics.^{[9][verification needed]} Theoretical statistics concerns both the logical arguments underlying justification of approaches to statistical inference, as well encompassing mathematical statistics. Mathematical statistics includes not only the manipulation of probability distributions necessary for deriving results related to methods of estimation and inference, but also various aspects of computational statistics and the design of experiments.

Statistics is closely related to probability theory, with which it is often grouped; the difference is roughly that in probability theory, one starts from the given parameters of a total population to deduce probabilities pertaining to samples, but statistical inference moves in the opposite direction, inductive inference from samples to the parameters of a larger or total population.

History[link]

The earliest writing on statistics was found in a 9th century book entitled: "Manuscript on Deciphering Cryptographic Messages", written by Al-Kindi (801–873 CE). In his book, Al-Kindi gave a detailed description of how to use statistics and frequency analysis to decipher encrypted messages, this was the birth of both statistics and cryptanalysis, according to Ibrahim Al-Kadi.^[10][11]

Some scholars pinpoint the origin of statistics to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt.^[12] Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and the natural and social sciences.

Its mathematical foundations were laid in the 17th century with the development of probability theory by Blaise Pascal and Pierre de Fermat. Probability theory arose from the study of games of chance. The method of least squares was first described by Carl Friedrich Gauss around 1794. The use of modern computers has expedited large-scale statistical computation, and has also made possible new methods that are impractical to perform manually.

Overview[link]

In applying statistics to a scientific, industrial, or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal". A population can also be composed of observations of a process at various times, with the data from each observation serving as a different member of the overall group. Data collected about this kind of "population" constitutes what is called a time series.

For practical reasons, a chosen subset of the population called a sample is studied — as opposed to compiling data about the entire group (an operation called census). Once a sample that is representative of the population is determined, data are collected for the sample members in an observational or experimental setting. This data can then be subjected to statistical analysis, serving two related purposes: description and inference.

• Descriptive statistics summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights), while frequency and percentage are more useful in terms of describing categorical data (like race).
• Inferential statistics uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis). Inference can extend to forecasting, prediction and estimation of unobserved values either in or associated with the population being studied; it can include extrapolation and interpolation of time series or spatial data, and can also include data mining.^[13]

The concept of correlation is particularly noteworthy for the potential confusion it can cause. Statistical analysis of a data set often reveals that two variables (properties) of the population under consideration tend to vary together, as if they were connected. For example, a study of annual income that also looks at age of death might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated; however, they may or may not be the cause of one another. The correlation phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable or confounding variable. For this reason, there is no way to immediately infer the existence of a causal relationship between the two variables. (See Correlation does not imply causation.)

For a sample to be used as a guide to an entire population, it is important that it is truly a representative of that overall population. Representative sampling assures that the inferences and conclusions can be safely extended from the sample to the population as a whole. A major problem lies in determining the extent to which the sample chosen is actually representative. Statistics offers methods to estimate and correct for any random trending within the sample and data collection procedures. There are also methods of experimental design for experiments that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.

Randomness is studied using the mathematical discipline of probability theory. Probability is used in "mathematical statistics" (alternatively, "statistical theory") to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method.

Misuse of statistics can produce subtle, but serious errors in description and interpretation — subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors. For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics. See below for further discussion.

Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data — which measures the extent to which a trend could be caused by random variation in the sample — may or may not agree with an intuitive sense of its significance. The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy.

Statistical methods[link]

Experimental and observational studies[link]

A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response. There are two major types of causal statistical studies: experimental studies and observational studies. In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies in how the study is actually conducted. Each can be very effective. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Instead, data are gathered and correlations between predictors and response are investigated.

Experiments[link]

The basic steps of a statistical experiment are:

1. Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability. Consideration of the selection of experimental subjects and the ethics of research is necessary. Statisticians recommend that experiments compare (at least) one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.
2. Design of experiments, using blocking to reduce the influence of confounding variables, and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage, the experimenters and statisticians write the experimental protocol that shall guide the performance of the experiment and that specifies the primary analysis of the experimental data.
3. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol.
4. Further examining the data set in secondary analyses, to suggest new hypotheses for future study.
5. Documenting and presenting the results of the study.

Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity. It turned out that productivity indeed improved (under the experimental conditions). However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness. The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself. Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.^{[citation needed]}

Observational study[link]

An example of an observational study is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a case-control study, and then look for the number of cases of lung cancer in each group.

Levels of measurement[link]

There are four main levels of measurement used in statistics: nominal, ordinal, interval, and ratio. Each of these have different degrees of usefulness in statistical research. Ratio measurements have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data.^{[citation needed]} Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit). Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values. Nominal measurements have no meaningful rank order among values.

Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature.

Key terms used in statistics[link]

Null hypothesis[link]

Interpretation of statistical information can often involve the development of a null hypothesis in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured.

The best illustration for a novice is the predicament encountered by a jury trial. The null hypothesis, H₀, asserts that the defendant is innocent, whereas the alternative hypothesis, H₁, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt. The H₀ (status quo) stands in opposition to H₁ and is maintained unless H₁ is supported by evidence"beyond a reasonable doubt". However,"failure to reject H₀" in this case does not imply innocence, but merely that the evidence was insufficient to convict. So the jury does not necessarily accept H₀ but fails to reject H₀. While one can not "prove" a null hypothesis one can test how close it is to being true with a power test, which tests for type II errors.

Error[link]

Working from a null hypothesis two basic forms of error are recognized:

• Type I errors where the null hypothesis is falsely rejected giving a "false positive".
• Type II errors where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative".

Error also refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean. Many statistical methods seek to minimize the mean-squared error, and these are called "methods of least squares."

Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important.

Interval estimation[link]

Most studies will only sample part of a population and so the results are not fully representative of the whole population. Any estimates obtained from the sample only approximate the population value. Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population. Often they are expressed as 95% confidence intervals. Formally, a 95% confidence interval for a value is a range where, if the sampling and analysis were repeated under the same conditions (yielding a different dataset), the interval would include the true (population) value 95% of the time. This does not imply that the probability that the true value is in the confidence interval is 95%. From the frequentist perspective, such a claim does not even make sense, as the true value is not a random variable. Either the true value is or is not within the given interval. However, it is true that, before any data are sampled and given a plan for how the confidence interval will be constructed, the probability is 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits of the interval are yet-to-be-observed random variables. One approach that does yield an interval that can be interpreted as having a given probability of containing the true value is to use a credible interval from Bayesian statistics: this approach depends on a different way of interpreting what is meant by "probability", that is as a Bayesian probability.

Significance[link]

This section includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations. (May 2012)

Statistics rarely give a simple Yes/No type answer to the question asked of them. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value).

Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug will be unlikely to help the patient in a noticeable way.

Criticisms arise because the hypothesis testing approach forces one hypothesis (the null hypothesis) to be "favored," and can also seem to exaggerate the importance of minor differences in large studies. A difference that is highly statistically significant can still be of no practical significance, but it is possible to properly formulate tests in account for this. (See also criticism of hypothesis testing.)

One response involves going beyond reporting only the significance level to include the p-value when reporting whether a hypothesis is rejected or accepted. The p-value, however, does not indicate the size of the effect. A better and increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as those of hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.

Examples[link]

Some well-known statistical tests and procedures are:

Specialized disciplines[link]

Statistical techniques are used in a wide range of types of scientific and social research, including: biostatistics, computational biology, computational sociology, network biology, social science, sociology and social research. Some fields of inquiry use applied statistics so extensively that they have specialized terminology. These disciplines include:

Statistics form a key basis tool in business and manufacturing as well. It is used to understand measurement systems variability, control processes (as in statistical process control or SPC), for summarizing data, and to make data-driven decisions. In these roles, it is a key tool, and perhaps the only reliable tool.

Statistical computing[link]

gretl, an example of an open source statistical package

The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of linear models, but powerful computers, coupled with suitable numerical algorithms, caused an increased interest in nonlinear models (such as neural networks) as well as the creation of new types, such as generalized linear models and multilevel models.

Increased computing power has also led to the growing popularity of computationally intensive methods based on resampling, such as permutation tests and the bootstrap, while techniques such as Gibbs sampling have made use of Bayesian models more feasible. The computer revolution has implications for the future of statistics with new emphasis on "experimental" and "empirical" statistics. A large number of both general and special purpose statistical software are now available.

Misuse[link]

This section includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations. (May 2012)

There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.^[15] A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie With Statistics^[15] outlines a range of considerations.

If various studies appear to contradict one another, then the public may come to distrust such studies. For example, one study may suggest that a given diet or activity raises blood pressure, while another may suggest that it lowers blood pressure. The discrepancy can arise from subtle variations in experimental design, such as differences in the patient groups or research protocols, which are not easily understood by the non-expert. (Media reports usually omit this vital contextual information entirely, because of its complexity.)

By choosing (or rejecting, or modifying) a certain sample, results can be manipulated. Such manipulations need not be malicious or devious; they can arise from unintentional biases of the researcher. The graphs used to summarize data can also be misleading.

Statistics applied to mathematics or the arts[link]

Traditionally, statistics was concerned with drawing inferences using a semi-standardized methodology that was "required learning" in most sciences. This has changed with use of statistics in non-inferential contexts. What was once considered a dry subject, taken in many fields as a degree-requirement, is now viewed enthusiastically. Initially derided by some mathematical purists, it is now considered essential methodology in certain areas.

• In number theory, scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns, which may then lead to hypotheses.
• Methods of statistics including predictive methods in forecasting, are combined with chaos theory and fractal geometry to create video works that are considered to have great beauty.
• The process art of Jackson Pollock relied on artistic experiments whereby underlying distributions in nature were artistically revealed. With the advent of computers, methods of statistics were applied to formalize such distribution driven natural processes, in order to make and analyze moving video art.
• Methods of statistics may be used predicatively in performance art, as in a card trick based on a Markov process that only works some of the time, the occasion of which can be predicted using statistical methodology.
• Statistics can be used to predicatively create art, as in the statistical or stochastic music invented by Iannis Xenakis, where the music is performance-specific. Though this type of artistry does not always come out as expected, it does behave in ways that are predictable and tunable using statistics.

See also[link]

Statistics portal

Find more about Statistics on Wikipedia's sister projects:
	Definitions and translations from Wiktionary
	Images and media from Commons
	Learning resources from Wikiversity
	News stories from Wikinews
	Quotations from Wikiquote
	Source texts from Wikisource
	Textbooks from Wikibooks

References[link]

This biographical article needs additional citations for verification. Please help by adding reliable sources. Contentious material about living persons that is unsourced or poorly sourced must be removed immediately, especially if potentially libelous or harmful. (March 2009)

Peter Donnelly, FRS (born 15 May 1959) is an Australian mathematician and Professor of Statistical Science at the University of Oxford. He is a specialist in applied probability and has made contributions to coalescent theory. His research group at Oxford has an international reputation for the development of statistical methodology to analyze genetic data.

He was educated at the University of Queensland and Balliol College, Oxford. When elected to a chair at Queen Mary College, London in 1988 he was only 29,^[1] and possibly the youngest Professor in Great Britain. He held a chair at the University of Chicago (1994–96) and was head of the Department of Statistics at the University of Oxford from 1996 to 2001. Since 2007, he has been Director, Wellcome Trust Centre for Human Genetics (WTCHG) in Oxford, where he is currently based. He is a fellow at St Anne's College, Oxford.

Many leading statistical geneticists worked with Donnelly as young researchers including David Balding, Carsten Wiuf, Matthew Stephens and Jonathan Pritchard. One area in which he has a leading reputation is in the interpretation of DNA evidence. He has acted as an expert witness on forensic science in criminal trials.^{[citation needed]}

He is noted for his collaborative work with biologists. He has been heavily involved in a number of large scale projects, such as the International HapMap Project and the Wellcome Trust Case Control Consortium, a genome-wide association study.^{[citation needed]}

He was elected a Fellow of the Royal Society in 2006 and also elected as a Fellow of the Academy of Medical Sciences in June 2008.

See also R v Adams

References[link]

1. ^ Peter Donnelly's CV

External links[link]

• TED Talks: Peter Donnelly shows how stats fool juries at TED Global in 2005

Hans Rosling
Rosling at the 2012 Time 100 gala
Born	(1948-07-27) July 27, 1948 (age 63)
Institutions	Karolinska Institute
Alma mater	Uppsala University, St. John's Medical College
Known for	Gapminder Foundation and Trendalyzer

Hans Rosling (born 27 July 1948^[1] in Uppsala, Sweden) is a Swedish medical doctor, academic, statistician and public speaker. He is Professor of International Health at Karolinska Institute^[2] and co-founder and chairman of the Gapminder Foundation, which developed the Trendalyzer software system.

Study and career[link]

From 1967 to 1974 Rosling studied statistics and medicine at Uppsala University, and in 1972 he studied public health at St. John's Medical College, Bangalore. He became a licenced physician in 1976 and from 1979 to 1981 he served as District Medical Officer in Nacala in northern Mozambique.

On 21 August 1981, Rosling discovered an outbreak of konzo, a paralytic disease,^[3][4] and the investigations that followed earned him a Ph.D. degree at Uppsala University in 1986. He spent two decades studying outbreaks of this disease in remote rural areas across Africa and supervised more than ten Ph.D. students^{[citation needed]}. Outbreaks occur among hunger-stricken rural populations in Africa where a diet dominated by insufficiently processed cassava results in simultaneous malnutrition and high dietary cyanide intake.^[4]

Rosling's research has also focused on other links between economic development, agriculture, poverty and health^[5] in Africa, Asia and Latin America. He has been health adviser to WHO, UNICEF and several aid agencies. In 1993 he was one of the initiators of Médecins Sans Frontières in Sweden. At Karolinska Institutet he was head of the Division of International Health (IHCAR) from 2001 to 2007. As chairman of Karolinska International Research and Training Committee (1998–2004) he started health research collaborations with universities in Asia, Africa, the Middle East and Latin America. He started new courses on Global Health and co-authored a textbook on Global Health that promotes a fact-based world view.

Rosling presented the television documentary The Joy of Stats, which was broadcast in the United Kingdom by BBC Four in December 2010.^[6]

Gapminder[link]

Rosling co-founded the Gapminder Foundation together with his son Ola Rosling and daughter-in-law Anna Rosling Rönnlund. Gapminder developed the Trendalyzer software that converts international statistics into moving, interactive graphics. His lectures using Gapminder graphics to visualise world development have won awards.^[7] The interactive animations are freely available from the Foundation's website. On 16 March 2007 Google acquired the Trendalyzer software with the intention to scale it up and make it freely available for public statistics. In 2008 Google made available a Motion Chart Google Gadget and in 2009 the Public Data Explorer.^[8]

Rosling is also a sword swallower, as demonstrated in the final moments of his second talk at the TED conference.^[9] In 2009 he was listed as one of 100 leading global thinkers by Foreign Policy Magazine,^[10] and in 2011 as one of 100 most creative people in business by the Fast Company Magazine.^[11] In 2011 he was elected member of the Swedish Academy of Engineering Sciences.

Awards[link]

Hans Rosling at the Swedish pavilion of Expo 2010 in Shanghai

• 2007 – Statistician of the Year Award from the Swedish Association for Statistics
• 2007 – Jubilee Prize from the Swedish Medical Society
• 2007 – Knowledge Prize from the National Encyclopaedia of Sweden
• 2008 – Speaker of the Year from the Swedish Event Academy
• 2008 – The Big Debate Award from Dagens Medicine
• 2009 – Georg and Greta Borgstrom Award from the Swedish Royal Academy of Agriculture and Forestry
• 2010 – Illis Quorum, Highest Award conferred by the Government of Sweden
• 2010 – The Grand Prize from the Royal Institute of Technology, Stockholm, Sweden
• 2010 – Gold Medal from Royal Swedish Academy of Engineering Sciences
• 2010 – World Technology Award in Design together with Ola Rosling & Anna Rosling Rönnlund
• 2010 – The Gannon Award for the Continued Pursuit of Human Advancement (US)^[12]
• 2011 - Grierson 2011 Award for the Best British Science Documentary, presenter in "The Joy of Stats"
• 2012 - Time 100 most influential people list

Selected publications[link]

• Thorson, A.; Ragnarsson, A.; Rosling, H.; Ekström, A. (2010). "Male circumcision reduces HIV transmission. The risk of transmission from woman to man is halved". Lakartidningen 107 (46): 2881–2883. PMID 21197783.  edit
• Hanson, S.; Thorson, A.; Rosling, H.; Örtendahl, C.; Hanson, C.; Killewo, J.; Ekström, A. M. (2009). Husereau, Don. ed. "Estimating the Capacity for ART Provision in Tanzania with the Use of Data on Staff Productivity and Patient Losses". PLoS ONE 4 (4): e5294. DOI:10.1371/journal.pone.0005294. PMC 2667213. PMID 19381270. //www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2667213.  edit
• Von Schreeb, J.; Riddez, L.; Samnegård, H.; Rosling, H. (2008). "Foreign field hospitals in the recent sudden-onset disasters in Iran, Haiti, Indonesia, and Pakistan". Prehospital and disaster medicine : the official journal of the National Association of EMS Physicians and the World Association for Emergency and Disaster Medicine in association with the Acute Care Foundation 23 (2): 144–151; discussion 151–3. PMID 18557294.  edit
• Schell, C. O.; Reilly, M.; Rosling, H.; Peterson, S.; Ekström, A. M. (2007). "Socioeconomic determinants of infant mortality: A worldwide study of 152 low-, middle-, and high-income countries". Scandinavian Journal of Public Health 35 (3): 288–297. DOI:10.1080/14034940600979171. PMID 17530551.  edit
• Von Schreeb, J.; Rosling, H.; Garfield, R. (2007). "Mortality in Iraq". The Lancet 369 (9556): 101. DOI:10.1016/S0140-6736(07)60058-0. PMID 17223462.  edit
• Elrayah, H.; Eltom, M.; Bedri, A.; Belal, A.; Rosling, H.; Ostenson, C. (2005). "Economic burden on families of childhood type 1 diabetes in urban Sudan". Diabetes Research and Clinical Practice 70 (2): 159–165. DOI:10.1016/j.diabres.2005.03.034. PMID 15919129.  edit
• Thanh, H. T. T.; Jiang, G. X.; Van, T. N.; Minh, D. P. T.; Rosling, H.; Wasserman, D. (2005). "Attempted suicide in Hanoi, Vietnam". Social Psychiatry and Psychiatric Epidemiology 40 (1): 64–71. DOI:10.1007/s00127-005-0849-6. PMID 15624077.  edit
• Rosling, H. (2004). "New map of world health is needed. North and South is changed to healthy and ill and West to rich and poor". Lakartidningen 101 (3): 198–201. PMID 14763091.  edit

References[link]

Wikiquote has a collection of quotations related to: Hans Rosling

External links[link]

• Hans Rosling on Twitter
• Hans Rosling shows the best stats you've ever seen at the 2006 TED Conference in Monterey, CA highlighting novel ways of presenting global statistics.
• Hans Rosling's new insights on poverty, from TED2007, featuring a surprise ending. (video)
• Video of Hans Rosling speech at the OECD World Forum in Istanbul
• Hans Rosling's blog
• Ola Rosling giving a Gapminder presentation at Google (video)
• Motion Chart Google Gadget
• Hans Rosling: Global health expert; data visionary profile on TED.com