![]() ![]() While this assumption is not as important with large samples, it is important with small sample sizes, especially less than 10. Normally distributed in the population (data should resemble a bell-shaped curve when plotted graphically)Īs with all parametric hypothesis testing, the one sample t test assumes that you have sampled your data from a population that follows a normal (or Gaussian) distribution. The results of a t test should be based on a random sample and only be generalized to the larger population from which samples were drawn. Obtained from a random sample of the population ![]() As they rely on the calculation of a mean value, variables that are categorical should not be analyzed using a t test. Prism cannot test this assumption, but there are graphical ways to explore data to verify this assumption is met.Ī t test is only appropriate to apply in situations where data represent variables that are continuous measurements. The results of a t test only make sense when the scatter is random - that whatever factor caused a value to be too high or too low affects only that one value. The term "error" refers to the difference between each value and the group mean. The one sample t test assumes that all "errors" in the data are independent. Independent (values are not related to one another): The null hypothesis for a one sample t test can be stated as: "The population mean equals the specified mean value." The alternative hypothesis for a one sample t test can be stated as: "The population mean is different from the specified mean value." ![]() Like all hypothesis testing, the one sample t test determines if there is enough evidence reject the null hypothesis (H0) in favor of an alternative hypothesis (H1). Often, this designated value is a mean previously established in a population, a standard value of interest, or a mean concluded from other studies. This designated value does not come from the data itself, but is an external value chosen for scientific reasons. The one sample t test, also referred to as a single sample t test, is a statistical hypothesis test used to determine whether the mean calculated from sample data collected from a single group is different from a designated value specified by the researcher. In this article you will learn the requirements and assumptions of a one sample t test, how to format and interpret the results of a one sample t test, and when to use different types of t tests. ![]()
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