In Bayesian statistics, a **credible interval** is an interval within which an unobserved parameter value falls with a particular subjective probability. It is an interval in the domain of a posterior probability distribution or a predictive distribution.^{[1]} The generalisation to multivariate problems is the **credible region**. Credible intervals are analogous to confidence intervals in frequentist statistics,^{[2]} although they differ on a philosophical basis;^{[3]} Bayesian intervals treat their bounds as fixed and the estimated parameter as a random variable, whereas frequentist confidence intervals treat their bounds as random variables and the parameter as a fixed value. Also, Bayesian credible intervals use (and indeed, require) knowledge of the situation-specific prior distribution, while the frequentist confidence intervals do not.

For example, in an experiment that determines the distribution of possible values of the parameter , if the subjective probability that lies between 35 and 45 is 0.95, then is a 95% credible interval.

Credible intervals are not unique on a posterior distribution. Methods for defining a suitable credible interval include:

- Choosing the narrowest interval, which for a unimodal distribution will involve choosing those values of highest probability density including the mode. This is sometimes called the
**highest posterior density interval**. - Choosing the interval where the probability of being below the interval is as likely as being above it. This interval will include the median. This is sometimes called the
**equal-tailed interval**. - Assuming that the mean exists, choosing the interval for which the mean is the central point.

It is possible to frame the choice of a credible interval within decision theory and, in that context, an optimal interval will always be a highest probability density set.^{[4]}

A frequentist 95% confidence interval means that with a large number of repeated samples, 95% of such calculated confidence intervals would include the true value of the parameter. In frequentist terms, the parameter is *fixed* (cannot be considered to have a distribution of possible values) and the confidence interval is *random* (as it depends on the random sample).

Bayesian credible intervals can be quite different from frequentist confidence intervals for two reasons:

- credible intervals incorporate problem-specific contextual information from the prior distribution whereas confidence intervals are based only on the data;
- credible intervals and confidence intervals treat nuisance parameters in radically different ways.

For the case of a single parameter and data that can be summarised in a single sufficient statistic, it can be shown that the credible interval and the confidence interval *will* coincide if the unknown parameter is a location parameter (i.e. the forward probability function has the form ), with a prior that is a uniform flat distribution;^{[5]} and also if the unknown parameter is a scale parameter (i.e. the forward probability function has the form ), with a Jeffreys' prior ^{[5]} — the latter following because taking the logarithm of such a scale parameter turns it into a location parameter with a uniform distribution.
But these are distinctly special (albeit important) cases; in general no such equivalence can be made.

**^**Edwards, Ward, Lindman, Harold, Savage, Leonard J. (1963) "Bayesian statistical inference in psychological research".*Psychological Review*,**70**, 193-242**^**Lee, P.M. (1997)*Bayesian Statistics: An Introduction*, Arnold. ISBN 0-340-67785-6**^**"Frequentism and Bayesianism".**^**O'Hagan, A. (1994)*Kendall's Advanced Theory of Statistics, Vol 2B, Bayesian Inference*, Section 2.51. Arnold, ISBN 0-340-52922-9- ^
^{a}^{b}Jaynes, E. T. (1976). "Confidence Intervals vs Bayesian Intervals", in*Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science*, (W. L. Harper and C. A. Hooker, eds.), Dordrecht: D. Reidel, pp. 175*et seq*

- Morey, R. D.; Hoekstra, R.; Rouder, J. N.; Lee, M. D.; Wagenmakers, E.-J. (2016). "The fallacy of placing confidence in confidence intervals".
*Psychonomic Bulletin & Review*.**23**(1): 103–123. doi:10.3758/s13423-015-0947-8. PMC 4742505. PMID 26450628.

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a component of research in all fields of study including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed.

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Methods engineeringMethods engineering is a subspecialty of industrial engineering and manufacturing engineering concerned with human integration in industrial production processes.

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Prediction intervals are used in both frequentist statistics and Bayesian statistics: a prediction interval bears the same relationship to a future observation that a frequentist confidence interval or Bayesian credible interval bears to an unobservable population parameter: prediction intervals predict the distribution of individual future points, whereas confidence intervals and credible intervals of parameters predict the distribution of estimates of the true population mean or other quantity of interest that cannot be observed.

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Statistical graphicsStatistical graphics, also known as graphical techniques, are graphics in the field of statistics used to visualize quantitative data.

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Statistical parameterA statistical parameter or population parameter is a quantity that indexes a family of probability distributions. It can be regarded as a numerical characteristic of a population or a statistical model.

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StatisticianA statistician is a person who works with theoretical or applied statistics. The profession exists in both the private and public sectors. It is common to combine statistical knowledge with expertise in other subjects, and statisticians may work as employees or as statistical consultants.

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