Question: The t-test makes several assumptions about the data that must be met prior to analysis. These assumptions need to be evaluated, because the accuracy of

The t-test makes several assumptions about the data that must be met prior to analysis. These assumptions need to be evaluated, because the accuracy of your interpretation of the data depends on whether assumptions are violated. The assumptions that Mary must meet about her data before she can correctly use an independent t-test to test the hypothesis are that the scale of measurement should be continuous or ordinal, random sampling must be used, data has a normal distribution, the larger the sample the lower the potential for error, and homogeneity of variance. How do you see if your data meets your assumptions? How much room do you have to violate any of these assumptions and still get accurate results from the t-test?

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