% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-calc.R
\name{icc_calc_f_test}
\alias{icc_calc_f_test}
\title{Hypothesis Testing for ICC}
\usage{
icc_calc_f_test(anova_result, icc_type, rho0 = 0, alpha = 0.05)
}
\arguments{
\item{anova_result}{List. Output from \code{icc_calc_anova}.}

\item{icc_type}{Character. ICC type code.}

\item{rho0}{Numeric. Null hypothesis ICC value, default 0.}

\item{alpha}{Numeric. Significance level, default 0.05.}
}
\value{
Named list with test results:
\describe{
  \item{H0}{Character. Null hypothesis statement.}
  \item{F_stat}{Numeric. F test statistic.}
  \item{df1}{Integer. Numerator degrees of freedom.}
  \item{df2}{Integer. Denominator degrees of freedom.}
  \item{p_value}{Numeric. One-tailed p-value (H1: ICC > rho0).}
}
}
\description{
Performs one-tailed F-test for ICC significance following McGraw & Wong (1996) Table 8.
Supports both null hypothesis of zero ICC and custom non-zero null value.
}
\references{
McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some
intraclass correlation coefficients. Psychological Methods, 1(1), 30-46.
}
\keyword{internal}
