Statistics

== Methods ==
{{more citations needed section|date=December 2020}}

=== Descriptive statistics ===
{{main|Descriptive statistics}}

A '''descriptive statistic''' (in the [[count noun]] sense) is a [[summary statistic]] that quantitatively describes or summarizes features of a collection of [[information]],<ref>{{cite book |last=Mann |first=Prem S. |year=1995 |title=Introductory Statistics |url=https://archive.org/details/introductorystat02edmann_z9s5 |url-access=registration |edition=2nd |publisher=Wiley |isbn=0-471-31009-3 }}</ref> while '''descriptive statistics''' in the [[mass noun]] sense is the process of using and analyzing those statistics. Descriptive statistics is distinguished from [[statistical inference|inferential statistics]] (or inductive statistics), in that descriptive statistics aims to summarize a [[Sample (statistics)|sample]], rather than use the data to learn about the [[statistical population|population]] that the sample of data is thought to represent.<ref>{{Cite web |title=Descriptive Statistics {{!}} Research Connections |url=https://www.researchconnections.org/research-tools/descriptive-statistics |access-date=2023-01-10 |website=www.researchconnections.org}}</ref>

=== Inferential statistics ===
{{main|Statistical inference}}

'''Statistical inference''' is the process of using [[data analysis]] to deduce properties of an underlying [[probability distribution]].<ref name="Oxford">Upton, G., Cook, I. (2008) ''Oxford Dictionary of Statistics'', OUP. {{ISBN|978-0-19-954145-4}}.</ref> Inferential statistical analysis infers properties of a [[Statistical population|population]], for example by testing hypotheses and deriving estimates.  It is assumed that the observed data set is [[Sampling (statistics)|sampled]] from a larger population. Inferential statistics can be contrasted with [[descriptive statistics]]. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.<ref>{{Cite web |title=Basic Inferential Statistics - Purdue OWL® - Purdue University |url=https://owl.purdue.edu/owl/research_and_citation/using_research/writing_with_statistics/basic_inferential_statistics.html |access-date=2023-01-10 |website=owl.purdue.edu}}</ref>

====Terminology and theory of inferential statistics====
=====Statistics, estimators and pivotal quantities=====
Consider [[Independent identically distributed|independent identically distributed (IID) random variables]] with a given [[probability distribution]]: standard [[statistical inference]] and [[estimation theory]] defines a [[random sample]] as the [[random vector]] given by the [[column vector]] of these IID variables.<ref name=Piazza>Piazza Elio, Probabilità e Statistica, Esculapio 2007</ref> The [[Statistical population|population]] being examined is described by a probability distribution that may have unknown parameters.

A statistic is a random variable that is a function of the random sample, but {{em|not a function of unknown parameters}}. The probability distribution of the statistic, though, may have unknown parameters. Consider now a function of the unknown parameter: an [[estimator]] is a statistic used to estimate such function. Commonly used estimators include [[sample mean]], unbiased [[sample variance]] and [[sample covariance]].

A random variable that is a function of the random sample and of the unknown parameter, but whose probability distribution ''does not depend on the unknown parameter'' is called a [[pivotal quantity]] or pivot. Widely used pivots include the [[z-score]], the [[Chi-squared distribution#Applications|chi square statistic]] and Student's [[Student's t-distribution#How the t-distribution arises|t-value]].

Between two estimators of a given parameter, the one with lower [[mean squared error]] is said to be more [[Efficient estimator|efficient]]. Furthermore, an estimator is said to be [[Unbiased estimator|unbiased]] if its [[expected value]] is equal to the [[true value]] of the unknown parameter being estimated, and asymptotically unbiased if its expected value converges at the [[Limit (mathematics)|limit]] to the true value of such parameter.

Other desirable properties for estimators include: [[UMVUE]] estimators that have the lowest variance for all possible values of the parameter to be estimated (this is usually an easier property to verify than efficiency) and [[consistent estimator]]s which [[converges in probability]] to the true value of such parameter.

This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed: the [[method of moments (statistics)|method of moments]], the [[maximum likelihood]] method, the [[least squares]] method and the more recent method of [[estimating equations]].

=====Null hypothesis and alternative hypothesis=====
Interpretation of statistical information can often involve the development of a [[null hypothesis]] which is usually (but not necessarily) that no relationship exists among variables or that no change occurred over time.<ref>{{cite book | last = Everitt | first = Brian | title = The Cambridge Dictionary of Statistics | publisher = Cambridge University Press | location = Cambridge, UK New York | year = 1998 | isbn = 0521593468 | url = https://archive.org/details/cambridgediction00ever_0 }}</ref><ref>{{cite web |url=http://www.yourstatsguru.com/epar/rp-reviewed/cohen1994/ |title=Cohen (1994) The Earth Is Round (p < .05) |publisher=YourStatsGuru.com |access-date=2015-07-20 |archive-date=2015-09-05 |archive-url=https://web.archive.org/web/20150905081658/http://www.yourstatsguru.com/epar/rp-reviewed/cohen1994/ |url-status=live }}</ref>

The best illustration for a novice is the predicament encountered by a criminal trial. The null hypothesis, H<sub>0</sub>, asserts that the defendant is innocent, whereas the alternative hypothesis, H<sub>1</sub>, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt. The H<sub>0</sub> (status quo) stands in opposition to H<sub>1</sub> and is maintained unless H<sub>1</sub> is supported by evidence "beyond a reasonable doubt". However, "failure to reject H<sub>0</sub>" in this case does not imply innocence, but merely that the evidence was insufficient to convict. So the jury does not necessarily ''accept'' H<sub>0</sub> but ''fails to reject'' H<sub>0</sub>. While one can not "prove" a null hypothesis, one can test how close it is to being true with a [[Statistical power|power test]], which tests for [[type II error]]s.

What [[statisticians]] call an [[alternative hypothesis]] is simply a hypothesis that contradicts the null hypothesis.

=====Error=====
Working from a [[null hypothesis]], two broad categories of error are recognized:
* [[Type I and type II errors#Type I error|Type I errors]] where the null hypothesis is falsely rejected, giving a "false positive".
* [[Type I and type II errors#Type II error|Type II errors]] where the null hypothesis fails to be rejected and an actual difference between populations is missed, giving a "false negative".

[[Standard deviation]] refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean, while [[Standard error (statistics)#Standard error of the mean|Standard error]] refers to an estimate of difference between sample mean and population mean.

A [[Errors and residuals in statistics#Introduction|statistical error]] is the amount by which an observation differs from its [[expected value]]. A [[Errors and residuals in statistics#Introduction|residual]] is the amount an observation differs from the value the estimator of the expected value assumes on a given sample (also called prediction).

[[Mean squared error]] is used for obtaining [[efficient estimators]], a widely used class of estimators. [[Root mean square error]] is simply the square root of mean squared error.

[[File:Linear least squares(2).svg|thumb|right|A least squares fit: in red the points to be fitted, in blue the fitted line.]]

Many statistical methods seek to minimize the [[residual sum of squares]], and these are called "[[least squares|methods of least squares]]" in contrast to [[Least absolute deviations]]. The latter gives equal weight to small and big errors, while the former gives more weight to large errors. Residual sum of squares is also [[Differentiable function|differentiable]], which provides a handy property for doing [[regression analysis|regression]]. Least squares applied to [[linear regression]] is called [[ordinary least squares]] method and least squares applied to [[nonlinear regression]] is called [[non-linear least squares]]. Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise. Both linear regression and non-linear regression are addressed in [[polynomial least squares]], which also describes the variance in a prediction of the dependent variable (y axis) as a function of the independent variable (x axis) and the deviations (errors, noise, disturbances) from the estimated (fitted) curve.

Measurement processes that generate statistical data are also subject to error.  Many of these errors are classified as [[Random error|random]] (noise) or [[Systematic error|systematic]] ([[bias]]), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of [[missing data]] or [[censoring (statistics)|censoring]] may result in [[bias (statistics)|biased estimates]] and specific techniques have been developed to address these problems.<ref>Rubin, Donald B.; Little, Roderick J.A., Statistical analysis with missing data, New York: Wiley 2002</ref>

=====Interval estimation=====
{{main|Interval estimation}}

[[File:NYW-confidence-interval.svg|thumb|right|[[Confidence intervals]]: the red line is true value for the mean in this example, the blue lines are random confidence intervals for 100 realizations.]]

Most studies only sample part of a population, so results do not 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 inference|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 variable]]s. 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 [[Probability interpretations|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.

=====Significance=====
{{main|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]]).

[[File:P-value in statistical significance testing.svg|upright=1.8|thumb|right|In this graph the black line is probability distribution for the [[test statistic]], the [[Critical region#Definition of terms|critical region]] is the set of values to the right of the observed data point (observed value of the test statistic) and the [[p-value]] is represented by the green area.]]

The standard approach<ref name="Piazza"/> is to test a null hypothesis against an alternative hypothesis. A [[Critical region#Definition of terms|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 does not 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.

Although in principle the acceptable level of statistical significance may be subject to debate, the [[significance level]] is the largest p-value that allows the test to reject the null hypothesis. This test 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 significance level, the lower the probability of committing type I error.

Some problems are usually associated with this framework (See [[Statistical hypothesis testing#Criticism|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|''p''-value]] when reporting whether a hypothesis is rejected or accepted. The p-value, however, does not indicate the [[effect size|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 interval]]s. 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 the 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]].<ref name=Ioannidis2005>{{Cite journal | last1 = Ioannidis | first1 = J.P.A. | author-link1 = John P.A. Ioannidis| title = Why Most Published Research Findings Are False | journal = PLOS Medicine | volume = 2 | issue = 8 | pages = e124 | year = 2005 | pmid = 16060722 | pmc = 1182327 | doi = 10.1371/journal.pmed.0020124 | doi-access = free }}</ref>
* 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.{{clarify|date=October 2016}}

=====Examples=====
Some well-known statistical [[Statistical hypothesis testing|tests]] and procedures are:

{{Columns-list|colwidth=22em|
* [[Analysis of variance]] (ANOVA)
* [[Chi-squared test]]
* [[Correlation]]
* [[Factor analysis]]
* [[Mann–Whitney (U)|Mann–Whitney ''U'']]
* [[Mean square weighted deviation]] (MSWD)
* [[Pearson product-moment correlation coefficient]]
* [[Regression analysis]]
* [[Spearman's rank correlation coefficient]]
* [[Student's t-test|Student's ''t''-test]]
* [[Time series analysis]]
* [[Conjoint Analysis]]
}}

===Exploratory data analysis===
{{main|Exploratory data analysis}}

'''Exploratory data analysis''' ('''EDA''') is an approach to [[data analysis|analyzing]] [[data set]]s to summarize their main characteristics, often with visual methods. A [[statistical model]] can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.