Confidence interval

Last updated
Each row of points is a sample from the same normal distribution. The colored lines are 50% confidence intervals for the mean, m. At the center of each interval is the sample mean, marked with a diamond. The blue intervals contain the mean, and the red ones do not. Normal distribution 50%25 CI illustration.svg
Each row of points is a sample from the same normal distribution. The colored lines are 50% confidence intervals for the mean, μ. At the center of each interval is the sample mean, marked with a diamond. The blue intervals contain the mean, and the red ones do not.

In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used. [1] [2] The confidence level represents the long-run proportion of corresponding CIs that contain the true value of the parameter. For example, out of all intervals computed at the 95% level, 95% of them should contain the parameter's true value. [3]


Factors affecting the width of the CI include the confidence level, the sample size, and the variability in the sample. [4] All else being the same, a larger sample would produce a narrower confidence interval. Likewise, greater variability in the sample produces a wider confidence interval, and a higher confidence level would demand a wider confidence interval. [5]


Let X be a random sample from a probability distribution with statistical parameter θ, which is a quantity to be estimated, and φ, representing quantities that are not of immediate interest. A confidence interval for the parameter θ, with confidence level or coefficient γ, is an interval determined by random variables and with the property:

The number γ, whose typical value is close to but not greater than 1, is sometimes given in the form (or as a percentage ), where is a small positive number, often 0.05 .

It is important for the bounds and to be specified in such a way that as long as X is collected randomly, every time we compute a confidence interval, there is probability γ that it would contain θ, the true value of the parameter being estimated. This should hold true for any actual θ and φ. [2]

Approximate confidence intervals

In many applications, confidence intervals that have exactly the required confidence level are hard to construct, but approximate intervals can be computed. The rule for constructing the interval may be accepted as providing a confidence interval at level if

to an acceptable level of approximation. Alternatively, some authors [6] simply require that

which is useful if the probabilities are only partially identified or imprecise, and also when dealing with discrete distributions. Confidence limits of form


are called conservative; [7] (p 210) accordingly, one speaks of conservative confidence intervals and, in general, regions.

Desired properties

When applying standard statistical procedures, there will often be standard ways of constructing confidence intervals. These will have been devised so as to meet certain desirable properties, which will hold given that the assumptions on which the procedure relies are true. These desirable properties may be described as: validity, optimality, and invariance.

Of the three, "validity" is most important, followed closely by "optimality". "Invariance" may be considered as a property of the method of derivation of a confidence interval, rather than of the rule for constructing the interval. In non-standard applications, these same desirable properties would be sought:


This means that the nominal coverage probability (confidence level) of the confidence interval should hold, either exactly or to a good approximation.


This means that the rule for constructing the confidence interval should make as much use of the information in the data-set as possible.

Recall that one could throw away half of a dataset and still be able to derive a valid confidence interval. One way of assessing optimality is by the length of the interval so that a rule for constructing a confidence interval is judged better than another if it leads to intervals whose lengths are typically shorter.


In many applications, the quantity being estimated might not be tightly defined as such.

For example, a survey might result in an estimate of the median income in a population, but it might equally be considered as providing an estimate of the logarithm of the median income, given that this is a common scale for presenting graphical results. It would be desirable that the method used for constructing a confidence interval for the median income would give equivalent results when applied to constructing a confidence interval for the logarithm of the median income: Specifically the values at the ends of the latter interval would be the logarithms of the values at the ends of former interval.

Methods of derivation

For non-standard applications, there are several routes that might be taken to derive a rule for the construction of confidence intervals. Established rules for standard procedures might be justified or explained via several of these routes. Typically a rule for constructing confidence intervals is closely tied to a particular way of finding a point estimate of the quantity being considered.

Summary statistics

This is closely related to the method of moments for estimation. A simple example arises where the quantity to be estimated is the population mean, in which case a natural estimate is the sample mean. Similarly, the sample variance can be used to estimate the population variance. A confidence interval for the true mean can be constructed centered on the sample mean with a width which is a multiple of the square root of the sample variance.

Likelihood theory

Estimates can be constructed using the maximum likelihood principle, the likelihood theory for this provides two ways of constructing confidence intervals or confidence regions for the estimates.

Estimating equations

The estimation approach here can be considered as both a generalization of the method of moments and a generalization of the maximum likelihood approach. There are corresponding generalizations of the results of maximum likelihood theory that allow confidence intervals to be constructed based on estimates derived from estimating equations.[ citation needed ]

Hypothesis testing

If hypothesis tests are available for general values of a parameter, then confidence intervals/regions can be constructed by including in the 100 p % confidence region all those points for which the hypothesis test of the null hypothesis that the true value is the given value is not rejected at a significance level of (1 − p) . [7] (§ 7.2 (iii))


In situations where the distributional assumptions for the above methods are uncertain or violated, resampling methods allow construction of confidence intervals or prediction intervals. The observed data distribution and the internal correlations are used as the surrogate for the correlations in the wider population.

Central limit theorem

The central limit theorem is a refinement of the law of large numbers. For a large number of independent identically distributed random variables with finite variance, the average approximately has a normal distribution, no matter what the distribution of the is, with the approximation roughly improving in proportion to [2]


Suppose {X1, …, Xn} is an independent sample from a normally distributed population with unknown parameters mean μ and variance σ2. Let

Where X is the sample mean, and S2 is the sample variance. Then

has a Student's t distribution with n − 1 degrees of freedom. [8] Note that the distribution of T does not depend on the values of the unobservable parameters μ and σ2; i.e., it is a pivotal quantity. Suppose we wanted to calculate a 95% confidence interval for μ. Then, denoting c as the 97.5th percentile of this distribution,

Note that "97.5th" and "0.95" are correct in the preceding expressions. There is a 2.5% chance that will be less than and a 2.5% chance that it will be larger than . Thus, the probability that will be between and is 95%.


and we have a theoretical (stochastic) 95% confidence interval for μ.

After observing the sample we find values x for X and s for S, from which we compute the confidence interval

In this bar chart, the top ends of the brown bars indicate observed means and the red line segments ("error bars") represent the confidence intervals around them. Although the error bars are shown as symmetric around the means, that is not always the case. In most graphs, the error bars do not represent confidence intervals (e.g., they often represent standard errors or standard deviations) Confidenceinterval.png
In this bar chart, the top ends of the brown bars indicate observed means and the red line segments ("error bars") represent the confidence intervals around them. Although the error bars are shown as symmetric around the means, that is not always the case. In most graphs, the error bars do not represent confidence intervals (e.g., they often represent standard errors or standard deviations)


Various interpretations of a confidence interval can be given (taking the 95% confidence interval as an example in the following).

Common misunderstandings

Plot of 50 confidence intervals from 50 samples generated from a normal distribution. Neyman Construction Confidence Intervals.png
Plot of 50 confidence intervals from 50 samples generated from a normal distribution.

Confidence intervals and levels are frequently misunderstood, and published studies have shown that even professional scientists often misinterpret them. [12] [13] [14] [15] [16] [17]

Deborah Mayo expands on this further as follows: [20]

It must be stressed, however, that having seen the value [of the data], Neyman–Pearson theory never permits one to conclude that the specific confidence interval formed covers the true value of 0 with either (1  α)100% probability or (1  α)100% degree of confidence. Seidenfeld's remark seems rooted in a (not uncommon) desire for Neyman–Pearson confidence intervals to provide something which they cannot legitimately provide; namely, a measure of the degree of probability, belief, or support that an unknown parameter value lies in a specific interval. Following Savage (1962), the probability that a parameter lies in a specific interval may be referred to as a measure of final precision. While a measure of final precision may seem desirable, and while confidence levels are often (wrongly) interpreted as providing such a measure, no such interpretation is warranted. Admittedly, such a misinterpretation is encouraged by the word 'confidence'.


Since confidence interval theory was proposed, a number of counter-examples to the theory have been developed to show how the interpretation of confidence intervals can be problematic, at least if one interprets them naïvely.

Confidence procedure for uniform location

Welch [21] presented an example which clearly shows the difference between the theory of confidence intervals and other theories of interval estimation (including Fisher's fiducial intervals and objective Bayesian intervals). Robinson [22] called this example "[p]ossibly the best known counterexample for Neyman's version of confidence interval theory." To Welch, it showed the superiority of confidence interval theory; to critics of the theory, it shows a deficiency. Here we present a simplified version.

Suppose that are independent observations from a Uniform(θ − 1/2, θ + 1/2) distribution. Then the optimal 50% confidence procedure for is [23]

A fiducial or objective Bayesian argument can be used to derive the interval estimate

which is also a 50% confidence procedure. Welch showed that the first confidence procedure dominates the second, according to desiderata from confidence interval theory; for every , the probability that the first procedure contains is less than or equal to the probability that the second procedure contains . The average width of the intervals from the first procedure is less than that of the second. Hence, the first procedure is preferred under classical confidence interval theory.

However, when , intervals from the first procedure are guaranteed to contain the true value : Therefore, the nominal 50% confidence coefficient is unrelated to the uncertainty we should have that a specific interval contains the true value. The second procedure does not have this property.

Moreover, when the first procedure generates a very short interval, this indicates that are very close together and hence only offer the information in a single data point. Yet the first interval will exclude almost all reasonable values of the parameter due to its short width. The second procedure does not have this property.

The two counter-intuitive properties of the first procedure—100% coverage when are far apart and almost 0% coverage when are close together—balance out to yield 50% coverage on average. However, despite the first procedure being optimal, its intervals offer neither an assessment of the precision of the estimate nor an assessment of the uncertainty one should have that the interval contains the true value.

This counter-example is used to argue against naïve interpretations of confidence intervals. If a confidence procedure is asserted to have properties beyond that of the nominal coverage (such as relation to precision, or a relationship with Bayesian inference), those properties must be proved; they do not follow from the fact that a procedure is a confidence procedure.

Confidence procedure for ω2

Steiger [24] suggested a number of confidence procedures for common effect size measures in ANOVA. Morey et al. [18] point out that several of these confidence procedures, including the one for ω2, have the property that as the F statistic becomes increasingly small—indicating misfit with all possible values of ω2—the confidence interval shrinks and can even contain only the single value ω2 = 0; that is, the CI is infinitesimally narrow (this occurs when for a CI).

This behavior is consistent with the relationship between the confidence procedure and significance testing: as F becomes so small that the group means are much closer together than we would expect by chance, a significance test might indicate rejection for most or all values of ω2. Hence the interval will be very narrow or even empty (or, by a convention suggested by Steiger, containing only 0). However, this does not indicate that the estimate of ω2 is very precise. In a sense, it indicates the opposite: that the trustworthiness of the results themselves may be in doubt. This is contrary to the common interpretation of confidence intervals that they reveal the precision of the estimate.


Confidence intervals were introduced by Jerzy Neyman in 1937. [25] Statisticians quickly took to the idea, but adoption by scientists was more gradual. Some authors in medical journals promoted confidence intervals as early as the 1970s. Despite this, confidence intervals were rarely used until the following decade, when they quickly became standard. [26] By the late 1980s, medical journals began to require the reporting of confidence intervals. [27]

See also

Confidence interval for specific distributions

Related Research Articles

In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule, the quantity of interest and its result are distinguished. For example, the sample mean is a commonly used estimator of the population mean.

The likelihood function is the joint probability of the observed data viewed as a function of the parameters of the chosen statistical model.

In statistics, the likelihood-ratio test assesses the goodness of fit of two competing statistical models based on the ratio of their likelihoods, specifically one found by maximization over the entire parameter space and another found after imposing some constraint. If the constraint is supported by the observed data, the two likelihoods should not differ by more than sampling error. Thus the likelihood-ratio test tests whether this ratio is significantly different from one, or equivalently whether its natural logarithm is significantly different from zero.

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

In statistics, a statistic is sufficient with respect to a statistical model and its associated unknown parameter if "no other statistic that can be calculated from the same sample provides any additional information as to the value of the parameter". In particular, a statistic is sufficient for a family of probability distributions if the sample from which it is calculated gives no additional information than the statistic, as to which of those probability distributions is the sampling distribution.

In statistics, point estimation involves the use of sample data to calculate a single value which is to serve as a "best guess" or "best estimate" of an unknown population parameter. More formally, it is the application of a point estimator to the data to obtain a point estimate.

In statistics, the power of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis when a specific alternative hypothesis is true. It is commonly denoted by , and represents the chances of a true positive detection conditional on the actual existence of an effect to detect. Statistical power ranges from 0 to 1, and as the power of a test increases, the probability of making a type II error by wrongly failing to reject the null hypothesis decreases.

<span class="mw-page-title-main">Consistent estimator</span> Statistical estimator converging in probability to a true parameter as sample size increases

In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ0—having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to θ0. This means that the distributions of the estimates become more and more concentrated near the true value of the parameter being estimated, so that the probability of the estimator being arbitrarily close to θ0 converges to one.

<span class="mw-page-title-main">Continuous uniform distribution</span> Uniform distribution on an interval

In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of symmetric probability distributions. The distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds. The bounds are defined by the parameters, a and b, which are the minimum and maximum values. The interval can either be closed or open. Therefore, the distribution is often abbreviated U, where U stands for uniform distribution. The difference between the bounds defines the interval length; all intervals of the same length on the distribution's support are equally probable. It is the maximum entropy probability distribution for a random variable X under no constraint other than that it is contained in the distribution's support.

This glossary of statistics and probability is a list of definitions of terms and concepts used in the mathematical sciences of statistics and probability, their sub-disciplines, and related fields. For additional related terms, see Glossary of mathematics and Glossary of experimental design.

In Bayesian statistics, a credible interval is an interval within which an unobserved parameter value falls with a particular probability. It is an interval in the domain of a posterior probability distribution or a predictive distribution. The generalisation to multivariate problems is the credible region.

In statistics, a binomial proportion confidence interval is a confidence interval for the probability of success calculated from the outcome of a series of success–failure experiments. In other words, a binomial proportion confidence interval is an interval estimate of a success probability p when only the number of experiments n and the number of successes nS are known.

In statistics, M-estimators are a broad class of extremum estimators for which the objective function is a sample average. Both non-linear least squares and maximum likelihood estimation are special cases of M-estimators. The definition of M-estimators was motivated by robust statistics, which contributed new types of M-estimators. The statistical procedure of evaluating an M-estimator on a data set is called M-estimation. 48 samples of robust M-estimators can be found in a recent review study.

Bootstrapping is any test or metric that uses random sampling with replacement, and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods.

Neyman construction, named after Jerzy Neyman, is a frequentist method to construct an interval at a confidence level such that if we repeat the experiment many times the interval will contain the true value of some parameter a fraction of the time.

Frequentist inference is a type of statistical inference based in frequentist probability, which treats “probability” in equivalent terms to “frequency” and draws conclusions from sample-data by means of emphasizing the frequency or proportion of findings in the data. Frequentist-inference underlies frequentist statistics, in which the well-established methodologies of statistical hypothesis testing and confidence intervals are founded.

Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural probability . The main focus is on the algorithms which compute statistics rooting the study of a random phenomenon, along with the amount of data they must feed on to produce reliable results. This shifts the interest of mathematicians from the study of the distribution laws to the functional properties of the statistics, and the interest of computer scientists from the algorithms for processing data to the information they process.

In statistics, the coverage probability is a technique for calculating a confidence interval which is the proportion of the time that the interval contains the true value of interest. For example, suppose our interest is in the mean number of months that people with a particular type of cancer remain in remission following successful treatment with chemotherapy. The confidence interval aims to contain the unknown mean remission duration with a given probability. This is the "confidence level" or "confidence coefficient" of the constructed interval which is effectively the "nominal coverage probability" of the procedure for constructing confidence intervals. The "nominal coverage probability" is often set at 0.95. The coverage probability is the actual probability that the interval contains the true mean remission duration in this example.

In statistical inference, the concept of a confidence distribution (CD) has often been loosely referred to as a distribution function on the parameter space that can represent confidence intervals of all levels for a parameter of interest. Historically, it has typically been constructed by inverting the upper limits of lower sided confidence intervals of all levels, and it was also commonly associated with a fiducial interpretation, although it is a purely frequentist concept. A confidence distribution is NOT a probability distribution function of the parameter of interest, but may still be a function useful for making inferences.

In particle physics, CLs represents a statistical method for setting upper limits on model parameters, a particular form of interval estimation used for parameters that can take only non-negative values. Although CLs are said to refer to Confidence Levels, "The method's name is ... misleading, as the CLs exclusion region is not a confidence interval." It was first introduced by physicists working at the LEP experiment at CERN and has since been used by many high energy physics experiments. It is a frequentist method in the sense that the properties of the limit are defined by means of error probabilities, however it differs from standard confidence intervals in that the stated confidence level of the interval is not equal to its coverage probability. The reason for this deviation is that standard upper limits based on a most powerful test necessarily produce empty intervals with some fixed probability when the parameter value is zero, and this property is considered undesirable by most physicists and statisticians.


  1. Zar, Jerrold H. (199). Biostatistical Analysis (4th ed.). Upper Saddle River, N.J.: Prentice Hall. pp. 43–45. ISBN   978-0130815422. OCLC   39498633.
  2. 1 2 3 Dekking, Frederik Michel; Kraaikamp, Cornelis; Lopuhaä, Hendrik Paul; Meester, Ludolf Erwin (2005). "A Modern Introduction to Probability and Statistics". Springer Texts in Statistics. doi:10.1007/1-84628-168-7. ISBN   978-1-85233-896-1. ISSN   1431-875X.
  3. Illowsky, Barbara. Introductory statistics. Dean, Susan L., 1945-, Illowsky, Barbara., OpenStax College. Houston, Texas. ISBN   978-1-947172-05-0. OCLC   899241574.
  4. Hazra, Avijit (October 2017). "Using the confidence interval confidently". Journal of Thoracic Disease. 9 (10): 4125–4130. doi:10.21037/jtd.2017.09.14. ISSN   2072-1439. PMC   5723800 . PMID   29268424.
  5. Khare, Vikas; Nema, Savita; Baredar, Prashant (2020). Ocean Energy Modeling and Simulation with Big Data Computational Intelligence for System Optimization and Grid Integration. ISBN   978-0-12-818905-4. OCLC   1153294021.
  6. Roussas, George G. (1997). A Course in Mathematical Statistics (2nd ed.). Academic Press. p. 397.
  7. 1 2 Cox, D.R.; Hinkley, D.V. (1974). Theoretical Statistics. Chapman & Hall.
  8. Rees. D.G. (2001) Essential Statistics, 4th Edition, Chapman and Hall/CRC. ISBN   1-58488-007-4 (Section 9.5)
  9. Cox D.R., Hinkley D.V. (1974) Theoretical Statistics, Chapman & Hall, p49, p209
  10. 1 2 Neyman, J. (1937). "Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability". Philosophical Transactions of the Royal Society A . 236 (767): 333–380. Bibcode:1937RSPTA.236..333N. doi: 10.1098/rsta.1937.0005 . JSTOR   91337.
  11. Cox D.R., Hinkley D.V. (1974) Theoretical Statistics, Chapman & Hall, pp 214, 225, 233
  12. Kalinowski, Pawel (2010). "Identifying Misconceptions about Confidence Intervals" (PDF). Retrieved 2021-12-22.
  13. "Archived copy" (PDF). Archived from the original (PDF) on 2016-03-04. Retrieved 2014-09-16.{{cite web}}: CS1 maint: archived copy as title (link)
  14. Hoekstra, R., R. D. Morey, J. N. Rouder, and E-J. Wagenmakers, 2014. Robust misinterpretation of confidence intervals. Psychonomic Bulletin Review, in press.
  15. Scientists’ grasp of confidence intervals doesn’t inspire confidence, Science News, July 3, 2014
  16. 1 2 Greenland, Sander; Senn, Stephen J.; Rothman, Kenneth J.; Carlin, John B.; Poole, Charles; Goodman, Steven N.; Altman, Douglas G. (April 2016). "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations". European Journal of Epidemiology. 31 (4): 337–350. doi:10.1007/s10654-016-0149-3. ISSN   0393-2990. PMC   4877414 . PMID   27209009.
  17. Helske, Jouni; Helske, Satu; Cooper, Matthew; Ynnerman, Anders; Besancon, Lonni (2021-08-01). "Can Visualization Alleviate Dichotomous Thinking? Effects of Visual Representations on the Cliff Effect". IEEE Transactions on Visualization and Computer Graphics. Institute of Electrical and Electronics Engineers (IEEE). 27 (8): 3397–3409. arXiv: 2002.07671 . doi:10.1109/tvcg.2021.3073466. ISSN   1077-2626. PMID   33856998. S2CID   233230810.
  18. 1 2 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.
  19. " Confidence Limits for the Mean". Archived from the original on 2008-02-05. Retrieved 2014-09-16.
  20. Mayo, D. G. (1981) "In defence of the Neyman–Pearson theory of confidence intervals", Philosophy of Science, 48 (2), 269–280. JSTOR   187185
  21. Welch, B. L. (1939). "On Confidence Limits and Sufficiency, with Particular Reference to Parameters of Location". The Annals of Mathematical Statistics. 10 (1): 58–69. doi: 10.1214/aoms/1177732246 . JSTOR   2235987.
  22. Robinson, G. K. (1975). "Some Counterexamples to the Theory of Confidence Intervals". Biometrika. 62 (1): 155–161. doi:10.2307/2334498. JSTOR   2334498.
  23. Pratt, J. W. (1961). "Book Review: Testing Statistical Hypotheses. by E. L. Lehmann". Journal of the American Statistical Association. 56 (293): 163–167. doi:10.1080/01621459.1961.10482103. JSTOR   2282344.
  24. Steiger, J. H. (2004). "Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis". Psychological Methods. 9 (2): 164–182. doi:10.1037/1082-989x.9.2.164. PMID   15137887.
  25. [Neyman, J., 1937. Outline of a theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 236(767), pp.333-380]
  26. Altman, Douglas G. (1991). "Statistics in medical journals: Developments in the 1980s". Statistics in Medicine. 10 (12): 1897–1913. doi:10.1002/sim.4780101206. ISSN   1097-0258. PMID   1805317.
  27. Sandercock, Peter A.G. (2015). "Short History of Confidence Intervals". Stroke. Ovid Technologies (Wolters Kluwer Health). 46 (8): e184-7. doi: 10.1161/strokeaha.115.007750 . ISSN   0039-2499. PMID   26106115.