Which statistical test compares a sample statistic to a hypothesized population mean?

The t-test is the appropriate statistical test used to compare a sample statistic to a hypothesized population mean. This test is valuable in situations where you want to determine if there is a statistically significant difference between the mean of a sample and a known or hypothesized population mean.

When conducting a t-test, you typically have a single sample mean and you’re testing it against a population mean, which helps in evaluating whether the observed sample mean is significantly different from what would be expected based on the population mean. The t-test addresses the uncertainty of estimating the population mean from the sample data, taking into account the variability in data and the sample size.

The other options are relevant in different contexts: ANOVA is used for comparing means across multiple groups; the Chi-square test is used for categorical data to assess how observed counts differ from expected counts; and regression analysis is employed to examine relationships between variables, not for comparing means directly. This understanding highlights why the t-test is specifically suited for the comparison of a sample mean to a hypothesized population mean.