Computes all results for a t-test. Note that the results may differ from stats::t.test(), see the "Details".
Either named parameters can be given, or a list with the parameters.
You must provide either x or mean, sd and n. If x is given then any values
given for mean, sd and n will be overwritten. Also either sd or sigma or both must be given.
x sample (default: numeric(0))
mean sample mean (default: mean(x))
n sample size (default: length(x))
sd sample standard deviation (default: sd(x))
sigma population standard deviation (default: NA = unknown)
mu0 true value of the mean (default: 0)
alternative a string specifying the alternative hypothesis (default: "two.sided"), otherwise "greater" or "less" can be used
alpha significance level (default: 0.05)
norm is the population normal distributed? (default: FALSE)
n.clt when the central limit theorem holds (default: getOption("n.clt", 30))
t2norm does the approximation \(t_n \approx N(0;1)\) hold? (default: NA= uset2norm` function)
ttest_num(..., arglist = NULL)A list with the input parameters and the following:
Xbar distribution of the random sampling function \(\bar{X}\), only available if sigma given
Statistic distribution of the test statistics
statistic test value
critical critical value(s)
criticalx critical value(s) in x range
acceptance0 acceptance interval for H0
acceptance0x acceptance interval for H0 in x range
accept1 is H1 accepted?
p.value p value for test
named input parameters
list: named input parameters, if given ... will be ignored
The results of ttest_num may differ from stats::t.test(). ttest_num is designed to return results
when you compute a t-test by hand. For example, for computing the test statistic the approximation \(t_n \approx N(0; 1)\)
is used if \(n>n.tapprox\). The p.value is computed from the cumulative distribution function of the normal or
the t distribution.
x <- runif(100)
ttest_num(x=x)
ttest_num(mean=mean(x), sd=sd(x), n=length(x))
ret <- ttest_num(x=x)
ret$alternative <- "less"
ttest_num(arglist=ret)
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