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# What does a T-test measure?

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The T-test is a statistical test that measures the significance of the difference between the means in two sets of data in relation to the variance of the data.

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The T-test can be used to determine whether two samples are statistically different when each small group of data is taken from distinct populations, the results from each group are not dependant on the results from the other group and the distribution of data in both groups is considered to be normal (determined by normality test). However, the T-test is not suitable for evaluation of three or more variables (analysis of variance is used in this case) or for evaluating large sets of data (the Z-test should be used for large samples).

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## Related Questions

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According to HealthKnowledge, the main disadvantage of parametric tests of significance is that the data must be normally distributed. The main advantage of parametric tests is that they provide information about the population in terms of parameters and confidence intervals. Another advantage of parametric tests is that they are easier to use in modeling (such as meta-regressions) than are non-parametric tests.

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Chi-square is a statistical test that detects whether an observed set of sample results are obtained by mere chance, or whether something other than chance is causing the data to vary. If the observed results sufficiently deviate from what is statistically expected, the calculated chi-square is statistically significant.

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In medical terms, the letter "C" with a line over it means "with," according to Taber's Online. Abbreviations are commonly used to communicate medical information in prescriptions and patient charts.