Type in Google’s function list

Statistical functions

Functions for averages, distributions, tests and trends. Google prints 136 Statistical function(s) in its Google Sheets function list.

Function	Syntax as printed	Description from Google Docs Editors Help
AVEDEV	`AVEDEV(value1, [value2, ...])`	Calculates the average of the magnitudes of deviations of data from a dataset's mean.
AVERAGE	`AVERAGE(value1, [value2, ...])`	Returns the numerical average value in a dataset, ignoring text.
AVERAGE.WEIGHTED	`AVERAGE.WEIGHTED(values, weights, [additional values], [additional weights])`	Finds the weighted average of a set of values, given the values and the corresponding weights.
AVERAGEA	`AVERAGEA(value1, [value2, ...])`	Returns the numerical average value in a dataset.
AVERAGEIF	`AVERAGEIF(criteria_range, criterion, [average_range])`	Returns the average of a range depending on criteria.
AVERAGEIFS	`AVERAGEIFS(average_range, criteria_range1, criterion1, [criteria_range2, criterion2, ...])`	Returns the average of a range depending on multiple criteria.
BETA.DIST	`BETA.DIST(value, alpha, beta, cumulative, lower_bound, upper_bound)`	Returns the probability of a given value as defined by the beta distribution function.
BETA.INV	`BETA.INV(probability, alpha, beta, lower_bound, upper_bound)`	Returns the value of the inverse beta distribution function for a given probability.
BETADIST	`BETADIST(value, alpha, beta, lower_bound, upper_bound)`	See BETA.DIST
BETAINV	`BETAINV(probability, alpha, beta, lower_bound, upper_bound)`	See BETA.INV
BINOM.DIST	`BINOM.DIST(num_successes, num_trials, prob_success, cumulative)`	See BINOMDIST
BINOM.INV	`BINOM.INV(num_trials, prob_success, target_prob)`	See CRITBINOM
BINOMDIST	`BINOMDIST(num_successes, num_trials, prob_success, cumulative)`	Calculates the probability of drawing a certain number of successes (or a maximum number of successes) in a certain number of tries given a population of a certain size containing a certain number of successes, with replacement of draws.
CHIDIST	`CHIDIST(x, degrees_freedom)`	Calculates the right-tailed chi-squared distribution, often used in hypothesis testing.
CHIINV	`CHIINV(probability, degrees_freedom)`	Calculates the inverse of the right-tailed chi-squared distribution.
CHISQ.DIST	`CHISQ.DIST(x, degrees_freedom, cumulative)`	Calculates the left-tailed chi-squared distribution, often used in hypothesis testing.
CHISQ.DIST.RT	`CHISQ.DIST.RT(x, degrees_freedom)`	Calculates the right-tailed chi-squared distribution, which is commonly used in hypothesis testing.
CHISQ.INV	`CHISQ.INV(probability, degrees_freedom)`	Calculates the inverse of the left-tailed chi-squared distribution.
CHISQ.INV.RT	`CHISQ.INV.RT(probability, degrees_freedom)`	Calculates the inverse of the right-tailed chi-squared distribution.
CHISQ.TEST	`CHISQ.TEST(observed_range, expected_range)`	See CHITEST
CHITEST	`CHITEST(observed_range, expected_range)`	Returns the probability associated with a Pearson’s chi-squared test on the two ranges of data. Determines the likelihood that the observed categorical data is drawn from an expected distribution.
CONFIDENCE	`CONFIDENCE(alpha, standard_deviation, pop_size)`	See CONFIDENCE.NORM
CONFIDENCE.NORM	`CONFIDENCE.NORM(alpha, standard_deviation, pop_size)`	Calculates the width of half the confidence interval for a normal distribution.
CONFIDENCE.T	`CONFIDENCE.T(alpha, standard_deviation, size)`	Calculates the width of half the confidence interval for a Student’s t-distribution.
CORREL	`CORREL(data_y, data_x)`	Calculates r, the Pearson product-moment correlation coefficient of a dataset.
COUNT	`COUNT(value1, [value2, ...])`	Returns a count of the number of numeric values in a dataset.
COUNTA	`COUNTA(value1, [value2, ...])`	Returns a count of the number of values in a dataset.
COVAR	`COVAR(data_y, data_x)`	Calculates the covariance of a dataset.
COVARIANCE.P	`COVARIANCE.P(data_y, data_x)`	See COVAR
COVARIANCE.S	`COVARIANCE.S(data_y, data_x)`	Calculates the covariance of a dataset, where the dataset is a sample of the total population.
CRITBINOM	`CRITBINOM(num_trials, prob_success, target_prob)`	Calculates the smallest value for which the cumulative binomial distribution is greater than or equal to a specified criteria.
DEVSQ	`DEVSQ(value1, value2)`	Calculates the sum of squares of deviations based on a sample.
EXPON.DIST	`EXPON.DIST(x, LAMBDA, cumulative)`	Returns the value of the exponential distribution function with a specified LAMBDA at a specified value.
EXPONDIST	`EXPONDIST(x, LAMBDA, cumulative)`	See EXPON.DIST
F.DIST	`F.DIST(x, degrees_freedom1, degrees_freedom2, cumulative)`	Calculates the left-tailed F probability distribution (degree of diversity) for two data sets with given input x. Alternately called Fisher-Snedecor distribution or Snedecor's F distribution.
F.DIST.RT	`F.DIST.RT(x, degrees_freedom1, degrees_freedom2)`	Calculates the right-tailed F probability distribution (degree of diversity) for two data sets with given input x. Alternately called Fisher-Snedecor distribution or Snedecor's F distribution.
F.INV	`F.INV(probability, degrees_freedom1, degrees_freedom2)`	Calculates the inverse of the left-tailed F probability distribution. Also called the Fisher-Snedecor distribution or Snedecor’s F distribution.
F.INV.RT	`F.INV.RT(probability, degrees_freedom1, degrees_freedom2)`	Calculates the inverse of the right-tailed F probability distribution. Also called the Fisher-Snedecor distribution or Snedecor’s F distribution.
F.TEST	`F.TEST(range1, range2)`	See FTEST
FDIST	`FDIST(x, degrees_freedom1, degrees_freedom2)`	See F.DIST.RT
FINV	`FINV(probability, degrees_freedom1, degrees_freedom2)`	See F.INV.RT
FISHER	`FISHER(value)`	Returns the Fisher transformation of a specified value.
FISHERINV	`FISHERINV(value)`	Returns the inverse Fisher transformation of a specified value.
FORECAST	`FORECAST(x, data_y, data_x)`	Calculates the expected y-value for a specified x based on a linear regression of a dataset.
FORECAST.LINEAR	`FORECAST.LINEAR(x, data_y, data_x)`	See FORECAST
FTEST	`FTEST(range1, range2)`	Returns the probability associated with an F-test for equality of variances. Determines whether two samples are likely to have come from populations with the same variance.
GAMMA	`GAMMA(number)`	Returns the Gamma function evaluated at the specified value.
GAMMA.DIST	`GAMMA.DIST(x, alpha, beta, cumulative)`	Calculates the gamma distribution, a two-parameter continuous probability distribution.
GAMMA.INV	`GAMMA.INV(probability, alpha, beta)`	The GAMMA.INV function returns the value of the inverse gamma cumulative distribution function for the specified probability and alpha and beta parameters.
GAMMADIST	`GAMMADIST(x, alpha, beta, cumulative)`	See GAMMA.DIST
GAMMAINV	`GAMMAINV(probability, alpha, beta)`	See GAMMA.INV
GAUSS	`GAUSS(z)`	The GAUSS function returns the probability that a random variable, drawn from a normal distribution, will be between the mean and z standard deviations above (or below) the mean.
GEOMEAN	`GEOMEAN(value1, value2)`	Calculates the geometric mean of a dataset.
HARMEAN	`HARMEAN(value1, value2)`	Calculates the harmonic mean of a dataset.
HYPGEOM.DIST	`HYPGEOM.DIST(num_successes, num_draws, successes_in_pop, pop_size)`	See HYPGEOMDIST
HYPGEOMDIST	`HYPGEOMDIST(num_successes, num_draws, successes_in_pop, pop_size)`	Calculates the probability of drawing a certain number of successes in a certain number of tries given a population of a certain size containing a certain number of successes, without replacement of draws.
INTERCEPT	`INTERCEPT(data_y, data_x)`	Calculates the y-value at which the line resulting from linear regression of a dataset will intersect the y-axis (x=0).
KURT	`KURT(value1, value2)`	Calculates the kurtosis of a dataset, which describes the shape, and in particular the "peakedness" of that dataset.
LARGE	`LARGE(data, n)`	Returns the nth largest element from a data set, where n is user-defined.
LOGINV	`LOGINV(x, mean, standard_deviation)`	Returns the value of the inverse log-normal cumulative distribution with given mean and standard deviation at a specified value.
LOGNORM.DIST	`LOGNORM.DIST(x, mean, standard_deviation)`	See LOGNORMDIST
LOGNORM.INV	`LOGNORM.INV(x, mean, standard_deviation)`	See LOGINV
LOGNORMDIST	`LOGNORMDIST(x, mean, standard_deviation)`	Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value.
MARGINOFERROR	`MARGINOFERROR(range, confidence)`	Calculates the amount of random sampling error given a range of values and a confidence level.
MAX	`MAX(value1, [value2, ...])`	Returns the maximum value in a numeric dataset.
MAXA	`MAXA(value1, value2)`	Returns the maximum numeric value in a dataset.
MAXIFS	`MAXIFS(range, criteria_range1, criterion1, [criteria_range2, criterion2], …)`	Returns the maximum value in a range of cells, filtered by a set of criteria.
MEDIAN	`MEDIAN(value1, [value2, ...])`	Returns the median value in a numeric dataset.
MIN	`MIN(value1, [value2, ...])`	Returns the minimum value in a numeric dataset.
MINA	`MINA(value1, value2)`	Returns the minimum numeric value in a dataset.
MINIFS	`MINIFS(range, criteria_range1, criterion1, [criteria_range2, criterion2], …)`	Returns the minimum value in a range of cells, filtered by a set of criteria.
MODE	`MODE(value1, [value2, ...])`	Returns the most commonly occurring value in a dataset.
MODE.MULT	`MODE.MULT(value1, value2)`	Returns the most commonly occurring values in a dataset.
MODE.SNGL	`MODE.SNGL(value1, [value2, ...])`	See MODE
NEGBINOM.DIST	`NEGBINOM.DIST(num_failures, num_successes, prob_success)`	See NEGBINOMDIST
NEGBINOMDIST	`NEGBINOMDIST(num_failures, num_successes, prob_success)`	Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials.
NORM.DIST	`NORM.DIST(x, mean, standard_deviation, cumulative)`	See NORMDIST
NORM.INV	`NORM.INV(x, mean, standard_deviation)`	See NORMINV
NORM.S.DIST	`NORM.S.DIST(x)`	See NORMSDIST
NORM.S.INV	`NORM.S.INV(x)`	See NORMSINV
NORMDIST	`NORMDIST(x, mean, standard_deviation, cumulative)`	Returns the value of the normal distribution function (or normal cumulative distribution function) for a specified value, mean, and standard deviation.
NORMINV	`NORMINV(x, mean, standard_deviation)`	Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation.
NORMSDIST	`NORMSDIST(x)`	Returns the value of the standard normal cumulative distribution function for a specified value.
NORMSINV	`NORMSINV(x)`	Returns the value of the inverse standard normal distribution function for a specified value.
PEARSON	`PEARSON(data_y, data_x)`	Calculates r, the Pearson product-moment correlation coefficient of a dataset.
PERCENTILE	`PERCENTILE(data, percentile)`	Returns the value at a given percentile of a dataset.
PERCENTILE.EXC	`PERCENTILE.EXC(data, percentile)`	Returns the value at a given percentile of a dataset, exclusive of 0 and 1.
PERCENTILE.INC	`PERCENTILE.INC(data, percentile)`	See PERCENTILE
PERCENTRANK	`PERCENTRANK(data, value, [significant_digits])`	Returns the percentage rank (percentile) of a specified value in a dataset.
PERCENTRANK.EXC	`PERCENTRANK.EXC(data, value, [significant_digits])`	Returns the percentage rank (percentile) from 0 to 1 exclusive of a specified value in a dataset.
PERCENTRANK.INC	`PERCENTRANK.INC(data, value, [significant_digits])`	Returns the percentage rank (percentile) from 0 to 1 inclusive of a specified value in a dataset.
PERMUT	`PERMUT(n, k)`	Returns the number of ways to choose some number of objects from a pool of a given size of objects, considering order.
PERMUTATIONA	`PERMUTATIONA(number, number_chosen)`	Returns the number of permutations for selecting a group of objects (with replacement) from a total number of objects.
PHI	`PHI(x)`	The PHI function returns the value of the normal distribution with mean 0 and standard deviation 1.
POISSON	`POISSON(x, mean, cumulative)`	See POISSON.DIST
POISSON.DIST	`POISSON.DIST(x, mean, [cumulative])`	Returns the value of the Poisson distribution function (or Poisson cumulative distribution function) for a specified value and mean.
PROB	`PROB(data, probabilities, low_limit, [high_limit])`	Given a set of values and corresponding probabilities, calculates the probability that a value chosen at random falls between two limits.
QUARTILE	`QUARTILE(data, quartile_number)`	Returns a value nearest to a specified quartile of a dataset.
QUARTILE.EXC	`QUARTILE.EXC(data, quartile_number)`	Returns value nearest to a given quartile of a dataset, exclusive of 0 and 4.
QUARTILE.INC	`QUARTILE.INC(data, quartile_number)`	See QUARTILE
RANK	`RANK(value, data, [is_ascending])`	Returns the rank of a specified value in a dataset.
RANK.AVG	`RANK.AVG(value, data, [is_ascending])`	Returns the rank of a specified value in a dataset. If there is more than one entry of the same value in the dataset, the average rank of the entries will be returned.
RANK.EQ	`RANK.EQ(value, data, [is_ascending])`	Returns the rank of a specified value in a dataset. If there is more than one entry of the same value in the dataset, the top rank of the entries will be returned.
RSQ	`RSQ(data_y, data_x)`	Calculates the square of r, the Pearson product-moment correlation coefficient of a dataset.
SKEW	`SKEW(value1, value2)`	Calculates the skewness of a dataset, which describes the symmetry of that dataset about the mean.
SKEW.P	`SKEW.P(value1, value2)`	Calculates the skewness of a dataset that represents the entire population.
SLOPE	`SLOPE(data_y, data_x)`	Calculates the slope of the line resulting from linear regression of a dataset.
SMALL	`SMALL(data, n)`	Returns the nth smallest element from a data set, where n is user-defined.
STANDARDIZE	`STANDARDIZE(value, mean, standard_deviation)`	Calculates the normalized equivalent of a random variable given mean and standard deviation of the distribution.
STDEV	`STDEV(value1, [value2, ...])`	Calculates the standard deviation based on a sample.
STDEV.P	`STDEV.P(value1, [value2, ...])`	See STDEVP
STDEV.S	`STDEV.S(value1, [value2, ...])`	See STDEV
STDEVA	`STDEVA(value1, value2)`	Calculates the standard deviation based on a sample, setting text to the value `0`.
STDEVP	`STDEVP(value1, value2)`	Calculates the standard deviation based on an entire population.
STDEVPA	`STDEVPA(value1, value2)`	Calculates the standard deviation based on an entire population, setting text to the value `0`.
STEYX	`STEYX(data_y, data_x)`	Calculates the standard error of the predicted y-value for each x in the regression of a dataset.
T.DIST	`T.DIST(x, degrees_freedom, cumulative)`	Returns the right tailed Student distribution for a value x.
T.DIST.2T	`T.DIST.2T(x, degrees_freedom)`	Returns the two tailed Student distribution for a value x.
T.DIST.RT	`T.DIST.RT(x, degrees_freedom)`	Returns the right tailed Student distribution for a value x.
T.INV	`T.INV(probability, degrees_freedom)`	Calculates the negative inverse of the one-tailed TDIST function.
T.INV.2T	`T.INV.2T(probability, degrees_freedom)`	Calculates the inverse of the two-tailed TDIST function.
T.TEST	`T.TEST(range1, range2, tails, type)`	Returns the probability associated with Student's t-test. Determines whether two samples are likely to have come from the same two underlying populations that have the same mean.
TDIST	`TDIST(x, degrees_freedom, tails)`	Calculates the probability for Student's t-distribution with a given input (x).
TINV	`TINV(probability, degrees_freedom)`	See T.INV.2T
TRIMMEAN	`TRIMMEAN(data, exclude_proportion)`	Calculates the mean of a dataset excluding some proportion of data from the high and low ends of the dataset.
TTEST	`TTEST(range1, range2, tails, type)`	See T.TEST
VAR	`VAR(value1, [value2, ...])`	Calculates the variance based on a sample.
VAR.P	`VAR.P(value1, [value2, ...])`	See VARP
VAR.S	`VAR.S(value1, [value2, ...])`	See VAR
VARA	`VARA(value1, value2)`	Calculates an estimate of variance based on a sample, setting text to the value `0`.
VARP	`VARP(value1, value2)`	Calculates the variance based on an entire population.
VARPA	`VARPA(value1, value2,...)`	Calculates the variance based on an entire population, setting text to the value `0`.
WEIBULL	`WEIBULL(x, shape, scale, cumulative)`	Returns the value of the Weibull distribution function (or Weibull cumulative distribution function) for a specified shape and scale.
WEIBULL.DIST	`WEIBULL.DIST(x, shape, scale, cumulative)`	See WEIBULL
Z.TEST	`Z.TEST(data, value, [standard_deviation])`	Returns the one-tailed P-value of a Z-test with standard distribution.
ZTEST	`ZTEST(data, value, [standard_deviation])`	See Z.TEST

Source: Google Docs Editors Help, “Google Sheets function list” (support.google.com/docs/table/25273), data fetched 28 September 2026. Data month: September 2026. Every function name in Google’s list is placed under one type; a function carries the type the list prints for it.