Performs common one-sample and two-sample hypothesis tests for means, proportions, and variances.
Usage
z_test_mu(
xbar,
mu0,
sigma = NULL,
n,
alpha = 0.05,
alternative = c("two.sided", "less", "greater"),
digits = 4,
quiet = FALSE,
s = NULL
)
t_test_mu(
xbar,
mu0,
s,
n,
alpha = 0.05,
alternative = c("two.sided", "less", "greater"),
var.equal = FALSE,
digits = 4,
quiet = FALSE,
paired = FALSE
)
p_test(
x,
n,
p0,
alpha = 0.05,
alternative = c("two.sided", "less", "greater"),
digits = 4,
quiet = FALSE,
check_npq = TRUE,
pooled = NULL,
continuity = FALSE
)
var_test_chisq(
s,
n,
sigma0 = NULL,
ratio0 = 1,
alpha = 0.05,
alternative = c("two.sided", "less", "greater"),
digits = 4,
quiet = FALSE
)Arguments
- xbar
Numeric vector of sample means or summary statistics.
- mu0
Null-hypothesis value for a mean or mean difference.
- sigma
Known population standard deviation input.
- n
Integer sample size or vector of sample sizes.
- alpha
Significance level in (0, 1).
- alternative
Alternative hypothesis direction:
"two.sided","less", or"greater".- digits
Integer number of decimal places used only for printed output.
- quiet
Logical; if
TRUE, suppress printed output.- s
Sample standard deviation input when sigma is unknown.
- var.equal
Logical; for
t_test_mu(), whether to use the pooled-variance test.- paired
Logical; for
t_test_mu(), whether the supplied summaries are for paired differences.- x
Count input for proportion and variance tests.
- p0
Null-hypothesis proportion value or difference in proportions.
- check_npq
Logical; whether to report normal-approximation adequacy checks for proportion tests.
- pooled
Logical; for two-sample proportion tests, whether to use pooled standard errors.
- continuity
Logical. If TRUE, apply a continuity correction in the two-sample z test for proportions. Ignored for one-sample exact tests.
- sigma0
Null-hypothesis standard deviation for the chi-square variance test.
- ratio0
Null-hypothesis variance ratio for the F test.