binom.test  R Documentation 
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 
p 
hypothesized probability of success. 
alternative 
indicates the alternative hypothesis and must be
one of 
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 confidence level
is at least conf.level
, but in general does not give the
shortestlength confidence intervals.
A list with class "htest"
containing the following components:
statistic 
the number of successes. 
parameter 
the number of trials. 
p.value 
the pvalue 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 
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. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.2307/2331986")}.
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
given proportions.
## 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.
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