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Independent T Test Assumptions. ASSUMPTIONS UNDERLYING THE INDEPENDENT-SAMPLES t TEST The data scores are independent of each other that is scores of one participant are not systematically related to scores of the other participants. The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. Experimental units only receive one treatment and they do not overlap. The one-sample t test is generally considered robust against violation of this assumption once N 30.
The independent variable IV is categorical with at least two levels groups The dependent variable DV is continuous which is measured on an interval or ratio scale. Independent sample t-test. An independent samples t-test requires the following assumptions. The Welchs t-test which is less restrictive compared to the original Students test. Conclusions from an independent samples t-test can be trusted if the following assumptions are met. Experimental units only receive one treatment and they do not overlap.
Assumptions for an Independent Samples T-Test.
The independent samples t-test comes in two different forms. The assumptions that the data must meet in order for the test results to be valid are. Random and Independent sampling. ASSUMPTIONS UNDERLYING THE INDEPENDENT-SAMPLES t TEST The data scores are independent of each other that is scores of one participant are not systematically related to scores of the other participants. This section shows the assumptions made by the different t-tests. You can use the equal variance version of the t-test even when the variance of one sample is twice the other or even more but when in doubt use the unequal variance version of the test.