Exact Binomial Test
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)
- number of successes, or a vector of length 2 giving the numbers of successes and failures, respectively.
- number of trials; ignored if
xhas length 2.
- hypothesized probability of success.
- indicates the alternative hypothesis and must be
"less". You can specify just the initial letter.
- 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 confidence level
is at least
conf.level, but in general does not give the
shortest-length confidence intervals.
- A list with class
"htest"containing the following components:
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,
alternative a character string describing the alternative hypothesis. 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 confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404--413.
William J. Conover (1971), Practical nonparametric statistics. New York: John Wiley & Sons. Pages 97--104.
Myles Hollander & Douglas A. Wolfe (1973), Nonparametric Statistical Methods. New York: John Wiley & Sons. Pages 15--22.
prop.test for a general (approximate) test for equal or
## Conover (1971), p. 97f. ## Under (the assumption of) simple Mendelian inheritance, a cross ## between plants of two particular genotypes produces progeny 1/4 of ## which are "dwarf" and 3/4 of which are "giant", respectively. ## In an experiment to determine if this assumption is reasonable, a ## cross results in progeny having 243 dwarf and 682 giant plants. ## If "giant" is taken as success, the null hypothesis is that p = ## 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.