binom.test package:stats R
_E_x_a_c_t _B_i_n_o_m_i_a_l _T_e_s_t
Performs an exact test of a simple null hypothesis about the
probability of success in a Bernoulli experiment.
binom.test(x, n, p = 0.5,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
x: number of successes, or a vector of length 2 giving the
numbers of successes and failures, respectively.
n: number of trials; ignored if 'x' has length 2.
p: hypothesized probability of success.
alternative: indicates the alternative hypothesis and must be one of
'"two.sided"', '"greater"' or '"less"'. You can specify
the initial letter.
conf.level: confidence level for the returned confidence interval.
Confidence intervals are obtained by a procedure first given in
Clopper and Pearson (1934). This guarantees that the
level is at least 'conf.level', but in general does not give
shortest-length confidence intervals.
A list with class '"htest"' containing the following
statistic: the number of successes.
parameter: the number of trials.
p.value: the p-value of the test.
conf.int: a confidence interval for the probability of success.
estimate: the estimated probability of success.
null.value: the probability of success under the null, 'p'.
alternative: a character string describing the alternative
method: the character string '"Exact binomial test"'.
data.name: a character string giving the names of the data.
Clopper, C. J. & Pearson, E. S. (1934). The use of
fiducial limits illustrated in the case of the binomial.
_Biometrika_, *26*, 404-413.
William J. Conover (1971), _Practical nonparametric
New York: John Wiley & Sons. Pages 97-104.
Myles Hollander & Douglas A. Wolfe (1973), _Nonparametric
Statistical Methods._ New York: John Wiley & Sons. Pages
'prop.test' for a general (approximate) test for equal or given
## Conover (1971), p. 97f.
## Under (the assumption of) simple Mendelian inheritance, a
## between plants of two particular genotypes produces progeny
## which are "dwarf" and 3/4 of which are "giant",
## In an experiment to determine if this assumption is
## cross results in progeny having 243 dwarf and 682 giant
## If "giant" is taken as success, the null hypothesis is that
## 3/4 and the alternative that p != 3/4.
binom.test(c(682, 243), p = 3/4)
binom.test(682, 682 + 243, p = 3/4) # The same.
## => Data are in agreement with the null hypothesis.