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- How to detrmine the appropriate test statistic.
- What exactly is a test statistic?When examining data samples, a test statistic quantifies the correctness of the expected data distribution in relation to the null hypothesis.
- The statistic is determined by the type of data analysis method used and reflects how well your data fits the projected distribution for the given test.
- Types of test statisticsThe following test statistics are some of the common applications data professionals use when performing statistical analysis:
- T-value The t-value is one type of test statistic that results from performing either t-tests or regression tests. Evaluating the t-value requires testing a null hypothesis where the means of both test samples are equal. A t-value may be calculated using a typical t-test using the formula:t = (X - 0) / (s / n), where X is the sample mean, 0 is the population mean, s is the sample standard deviation, and n is the sample size.
- Z-valueAnother typical test statistic where the null hypothesis says that the means of two populations are equal is the z-value. This statistic goes beyond the t-value, which only examines a subset of the population. You may use the following formula to calculate the z-value:z = (X - ) /, where X represents the raw data or score, is the population mean, and is the population standard deviation.
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