Below you will find complete descriptions and links to 24 different analytics calculators for computing a variety of p-values (probability values).

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Compute the exact two-tailed and hypergeometric probabilities of obtaining a particular distribution of values in a 2x2 contingency table using Fisher's exact test, given the number of items or observations in each cell. Knowing the exact probability of observing a given distribution of values can be very useful in analytics studies that rely on categorical data.

Compute the exact two-tailed probability of obtaining a particular distribution of values in a 2x3 contingency table using the Freeman-Halton extension to Fisher's exact test, given the number of items or observations in each cell. Knowing the exact probability of observing a given distribution of values can be very useful in analytics studies that rely on categorical data.

Compute the exact two-tailed probability of obtaining a particular distribution of values in a 3x3 contingency table using the Freeman-Halton extension to Fisher's exact test, given the number of items or observations in each cell. Knowing the exact probability of observing a given distribution of values can be very useful in analytics studies that rely on categorical data.

Compute the p-value for an analysis of variance (ANOVA) study, given the ANOVA study's F-value, between (or treatment) degrees of freedom, and within (or residual / error) degrees of freedom. Knowing the probability value associated with an analysis of variance is critical to assessing hypotheses in analytics studies that rely on ANOVA models.

Compute a binomial probability (that is, the probability of an individual binomial outcome), given the number of trials, the number of observed successes, and the probability of a successful outcome occurring. Knowing how likely it is that an individual binomial outcome will occur is very useful for analytics studies that involve binomial experiments.

Compute the one-tailed (right-tail) probability value for a chi-square test, given the chi-square value and the degrees of freedom. Knowing the probability level associated with a particular chi-square value is often very useful in analytics studies that rely on categorical data.

Compute the complementary probability of an event A (that is, the probability that event A will not occur), given the probability of event A occurring. Knowing the complementary probability of an event is often very useful in analytics studies that examine event occurrence.

Compute the conditional probability of an event A (that is, the probability of event A occurring, given that event B has occurred), given the probability of event B, and the joint probability of events A and B. Knowing how the probability of one event changes when another event has occurred can be very useful in analytics studies that examine event occurrence.

Compute the probability associated with a specified interval under the continuous uniform distribution, given the upper and lower limits of the distribution, and the upper and lower limits of the probability interval. Knowing the probability associated with a particular range of values under a continuous uniform distribution is often very useful in analytics studies that involve uniformly distributed data.

Compute the one-tailed and two-tailed probability values for a Pearson correlation coefficient, given the sample size and the correlation value r. Knowing the significance level for a correlation coefficient is very useful for understanding the relationship between two variables in an analytics study.

Compute cumulative binomial probabilities (that is, the cumulative probability associated with a binomial outcome), given the number of trials, the number of observed successes, and the probability of a successful outcome occurring. The calculator will compute P(X<x), P(X≤x), P(X>x), and P(X≥x). Knowing the cumulative probability associated with a binomial outcome can be very useful for analytics studies that involve binomial experiments.

Compute cumulative probabilities for a Poisson outcome, given the expected number of event occurrences per interval, the total number of events observed, and the total number of intervals. The calculator will compute P(K<k), P(K≤k), P(K>k), and P(K≥k) for the total number of observed event occurrences *k*. Knowing the cumulative probabilities for a Poisson outcome is often very useful in analytics studies that use Poisson experiments.

Compute the probability value for an F-test, given the F-value, and the numerator and denominator degrees of freedom. Knowing the probability level associated with a particular F-value is often very useful for making decisions about the value of statistical models in analytics studies.

Compute the joint probability of two events A and B (that is, the probability of A and B occurring together), given the probability of event B, and the conditional probability of event A. Knowing how likely it is that two events will occur together can be very useful in analytics studies that examine event occurrence.

Compute the F-value and associated p-value for a multiple regression study, given the model R-square, the number of predictor variables, and the total sample size. F and p-values can be very useful ways of assessing and comparing different regression models when performing analytics.

Compute a Poisson probability, given the expected number of event occurrences per interval, the total number of events observed, and the total number of intervals. Knowing the probability of a specific number of events occurring within a specific number of fixed intervals is often very useful in analytics studies that use Poisson-distributed data.

Compute the one-tailed and two-tailed probabilities that the indirect effect of an independent variable on a dependent variable through a mediator variable is significant by using the Sobel test. Many analytics studies rely on mediation models, and identifying whether a mediator variable significantly carries the influence of an independent variable to a dependent variable is critical when assessing the value of such models.

Compute the extent to which two correlation coefficients are significantly different from one another, given the values of the two correlation coefficients and their associated sample sizes. The calculator computes the z-score for the significance test and the p-value. Knowing whether two correlation coefficients are significantly different from one another can be very useful in analytics studies that compare multiple groups.

Compute the extent to which the slopes of two lines are significantly different from one another, given each line's slope, standard error, and sample size. The calculator computes the t-value for the significance test, the degrees of freedom, and the p-value. Knowing whether the slopes of two lines are significantly different from one another can be very useful in analytics studies that compare multiple groups.

Compute the cumulative area under the standard normal distribution, given a z-score. The standard normal distribution is used frequently in analytics, and it is often very useful to know the cumulative probability for the distribution from minus infinity to the z-score.

Compute the one-tailed (right-tail) area under the standard normal distribution associated with a given z-score. Knowing the one-tailed probability for a particular z-score can be useful in a wide variety of analytics techniques.

Compute the two-tailed area under the standard normal distribution that is associated with +/- a given z-score. Knowing the two-tailed probability for a particular z-score can be useful in a wide variety of analytics techniques.

Compute the one-tailed and two-tailed probability values for a t-test, given the t-value and the degrees of freedom. Knowing the probability values for a t-test is often very useful in analytics when making decisions about hypotheses or the usefulness of a predictor variable.

Compute the union probability of two events A and B (that is, the probability that either A or B, or both A and B will occur), given the probability of event A, the probability of event B, and the joint probability of events A and B. Knowing how likely it is that one or both events will occur can be very useful in analytics studies that examine event occurrence.