In hypothesis testing, we must state the assumed value of the population parameter called the null hypothesis. The goal of hypothesis testing is to verify if the sample data is part of the population of interest. You either have sufficient evidence to accept the null hypothesis or reject it—you do not prove it. The significance level or p-value indicates the likelihood that the sample comes from the population of interest. Statisticians usually use a p-value of 0.05 as the cutoff for statistical significance. In other words, a p-value less than 0.05 is sufficient evidence to reject the null hypothesis. Typically, the null hypothesis is a statement about the value of the population parameter. For example, μ = 100 versus μ ≠ 100. A one-sided test means we are testing the null hypothesis of either less than or greater than. A two-sided test means we are testing the null hypothesis of less than and greater than.ONE-SAMPLE T-TEST
The one-sample t-test is used to compare a sample mean to a hypothesized population mean. The hypothesis can be either a one-sided or two-sided test. Usually, the population variance is unknown requiring use of the t-distribution, which takes into account the uncertainty in estimating the sample variance. The t-distribution is tabled by confidence level and degrees of freedom. For the one-sample t-test, the degrees of freedom are the number of observations used to estimate the sample standard deviation minus one. The formula for the one-sample t-test is as follows:
in which X-mean is the sample mean, μ is the theoretical population mean, s is the sample standard deviation, and n is the sample size used to estimate the mean and standard deviation.