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Interval estimation

Main article: Interval estimation

Most studies only sample part of a population, so results don't fully represent 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 in 95% of all possible cases. 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 to construct the confidence interval, 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.

In principle confidence intervals can be symmetrical or asymmetrical. An interval can be asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or right sided interval), but it can also be asymmetrical because the two sided interval is built violating symmetry around the estimate. Sometimes the bounds for a confidence interval are reached asymptotically and these are used to approximate the true bounds.


Main article: Statistical significance

Statistics rarely give a simple Yes/No type answer to the question under analysis. 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).

The standard approach¹⁹ is to test a null hypothesis against an alternative hypothesis. A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator doesn't belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false.

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 is unlikely to help the patient noticeably.

While in principle the acceptable level of statistical significance may be subject to debate, the p-value is the smallest significance level that allows the test to reject the null hypothesis. This is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the p-value, the lower the probability of committing type I error.

Some problems are usually associated with this framework (See criticism of hypothesis testing):

- A difference that is highly statistically significant can still be of no   practical significance, but it is possible to properly formulate tests to   account for this. 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 or importance of the observed effect and can also seem   to exaggerate the importance of minor differences in large studies. 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. - Fallacy of the transposed conditional, aka prosecutor's fallacy:   criticisms arise because the hypothesis testing approach forces one   hypothesis (the null hypothesis) to be favored, since what is being   evaluated is probability of the observed result given the null hypothesis  and not probability of the null hypothesis given the observed result. An   alternative to this approach is offered by Bayesian inference, although   it requires establishing a prior probability.²³ - Rejecting the null hypothesis does not automatically prove the   alternative hypothesis. - As everything in inferential statistics it relies on sample size, and   therefore under fat tails p-values may be seriously mis-computed.


Some well-known statistical tests and procedures are:

- Analysis of variance (ANOVA) - Chi-squared test - Correlation - Factor analysis - Mann–Whitney U - Mean square weighted deviation (MSWD) - Pearson product-moment correlation coefficient - Regression analysis - Spearman's rank correlation coefficient - Student's t-test - Time series analysis - Conjoint Analysis

Misuse of statistics

Main article: Misuse of statistics

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.

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.

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.²⁴ 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 Statistics²⁴ outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).²⁵

Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.²⁶ Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias.²⁷ Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs.²⁶ Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented.²⁷ To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole.²⁸ According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism."²⁹

To assist in the understanding of statistics Huff proposed a series of questions to be asked in each case:³⁰

- Who says so? (Does he/she have an axe to grind?) - How does he/she know? (Does he/she have the resources to know the facts?) - What's missing? (Does he/she give us a complete picture?) - Did someone change the subject? (Does he/she offer us the right answer to   the wrong problem?) - Does it make sense? (Is his/her conclusion logical and consistent with   what we already know?)

Misinterpretation: correlation

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.)

History of statistical science

Main articles: History of statistics and Founders of statistics

Statistical methods date back at least to the 5th century BC.

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.³¹ 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 natural and social sciences.

Its mathematical foundations were laid in the 17th century with the development of the probability theory by Gerolamo Cardano, Blaise Pascal and Pierre de Fermat. Mathematical probability theory arose from the study of games of chance, although the concept of probability was already examined in medieval law and by philosophers such as Juan Caramuel.³² The method of least squares was first described by Adrien-Marie Legendre in 1805.

The modern field of statistics emerged in the late 19th and early 20th century in three stages.³³ The first wave, at the turn of the century, was led by the work of Francis Galton and Karl Pearson, who transformed statistics into a rigorous mathematical discipline used for analysis, not just in science, but in industry and politics as well. Galton's contributions included introducing the concepts of standard deviation, correlation, regression analysis and the application of these methods to the study of the variety of human characteristics – height, weight, eyelash length among others.³⁴ Pearson developed the Pearson product-moment correlation coefficient, defined as a product-moment,³⁵ the method of moments for the fitting of distributions to samples and the Pearson distribution, among many other things.³⁶ Galton and Pearson founded Biometrika as the first journal of mathematical statistics and biostatistics (then called biometry), and the latter founded the world's first university statistics department at University College London.³⁷

Ronald Fisher coined the term null hypothesis during the Lady tasting tea experiment, which "is never proved or established, but is possibly disproved, in the course of experimentation".³⁸ ³⁹

The second wave of the 1910s and 20s was initiated by William Gosset, and reached its culmination in the insights of Ronald Fisher, who wrote the textbooks that were to define the academic discipline in universities around the world. Fisher's most important publications were his 1918 seminal paper The Correlation between Relatives on the Supposition of Mendelian Inheritance, which was the first to use the statistical term, variance, his classic 1925 work Statistical Methods for Research Workers and his 1935 The Design of Experiments,⁴⁰ ⁴¹ ⁴² ⁴³ where he developed rigorous design of experiments models. He originated the concepts of sufficiency, ancillary statistics, Fisher's linear discriminator and Fisher information.⁴⁴ In his 1930 book The Genetical Theory of Natural Selection he applied statistics to various biological concepts such as Fisher's principle⁴⁵ ). Nevertheless, A. W. F. Edwards has remarked that it is "probably the most celebrated argument in evolutionary biology".⁴⁵ (about the sex ratio), the Fisherian runaway,⁴⁶ ⁴⁷ ⁴⁸ ⁴⁹ ⁵⁰ ⁵¹ a concept in sexual selection about a positive feedback runaway affect found in evolution.

The final wave, which mainly saw the refinement and expansion of earlier developments, emerged from the collaborative work between Egon Pearson and Jerzy Neyman in the 1930s. They introduced the concepts of "Type II" error, power of a test and confidence intervals. Jerzy Neyman in 1934 showed that stratified random sampling was in general a better method of estimation than purposive (quota) sampling.⁵²

Today, statistical methods are applied in all fields that involve decision making, for making accurate inferences from a collated body of data and for making decisions in the face of uncertainty based on statistical methodology. The use of modern computers has expedited large-scale statistical computations, and has also made possible new methods that are impractical to perform manually. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.⁵³


Applied statistics, theoretical statistics and mathematical statistics

"Applied statistics" comprises descriptive statistics and the application of inferential statistics.⁵⁴ ⁵⁵ 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.

Machine learning and data mining

There are two applications for machine learning and data mining: data management and data analysis. Statistics tools are necessary for the data analysis.

Statistics in society

Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Statistical consultants can help organizations and companies that don't have in-house expertise relevant to their particular questions.

Statistical computing

Main article: Computational statistics

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.

Statistics applied to mathematics or the arts

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, statistical methods were applied to formalize such   distribution-driven natural processes 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.

Specialized disciplines

Main article: List of fields of application of statistics

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:

- Actuarial science (assesses risk in the insurance and finance industries) - Applied information economics - Astrostatistics (statistical evaluation of astronomical data) - Biostatistics - Business statistics - Chemometrics (for analysis of data from chemistry) - Data mining (applying statistics and pattern recognition to discover   knowledge from data) - Data science - Demography - Econometrics (statistical analysis of economic data) - Energy statistics - Engineering statistics - Epidemiology (statistical analysis of disease) - Geography and Geographic Information Systems, specifically in Spatial   analysis - Image processing - Medical Statistics - Psychological statistics - Reliability engineering - Social statistics - Statistical Mechanics

In addition, there are particular types of statistical analysis that have also developed their own specialised terminology and methodology:

- Bootstrap / Jackknife resampling - Multivariate statistics - Statistical classification - Structured data analysis (statistics) - Structural equation modelling - Survey methodology - Survival analysis - Statistics in various sports, particularly baseball - known as   Sabermetrics - and cricket

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), form summarizing data, and to make data-driven decisions. In these roles, it is a key tool, and perhaps the only reliable tool.


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