Stata 15 help for bitest

[R] bitest -- Binomial probability test


Binomial probability test

bitest varname == #p [if] [in] [weight] [, detail]

Immediate form of binomial probability test

bitesti #N #succ #p [, detail]

by is allowed with bitest; see [D] by.

fweights are allowed with bitest; see weight.



Statistics > Summaries, tables, and tests > Classical tests of hypotheses > Binomial probability test


Statistics > Summaries, tables, and tests > Classical tests of hypotheses > Binomial probability test calculator


bitest performs exact hypothesis tests for binomial random variables. The null hypothesis is that the probability of a success on a trial is #p. The total number of trials is the number of nonmissing values of varname (in bitest) or #N (in bitesti). The number of observed successes is the number of 1s in varname (in bitest) or #succ (in bitesti). varname must contain only 0s, 1s, and missing.

bitesti is the immediate form of bitest; see immed for a general introduction to immediate commands.


+----------+ ----+ Advanced +---------------------------------------------------------

detail shows the probability of the observed number of successes, k_obs; the probability of the number of successes on the opposite tail of the distribution that is used to compute the two-sided p-value, k_opp; and the probability of the point next to k_opp. This information can be safely ignored. See the technical note in [R] bitest for details.


Setup . webuse quick

Test whether probability of success equals 0.3 . bitest quick == 0.3 . bitest quick == 0.3, detail

Test if probability of success = 0.5, given 3 successes in 10 trials . bitesti 10 3 .5

Test if probability of success = 0.000001, given 36 successes in 2.5 million trials . bitesti 2500000 36 .000001

Stored results

bitest and bitesti store the following in r():

Scalars r(N) number N of trials r(P_p) assumed probability p of success r(k) observed number k of successes r(p_l) lower one-sided p-value r(p_u) upper one-sided p-value r(p) two-sided p-value r(k_opp) opposite extreme k r(P_k) probability of observed k (detail only) r(P_oppk) probability of opposite extreme k (detail only) r(k_nopp) k next to opposite extreme (detail only) r(P_noppk) probability of k next to opposite extreme (detail only)

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