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How do you test for normality in an independent t-test?
The normality assumption can be checked by computing the Shapiro-Wilk test for each group. If the data is normally distributed, the p-value should be greater than 0.05.
What are the assumptions for an independent samples t-test?
The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.
What are the assumptions for a two-sample independent t test?
Two-sample t-test assumptions Data in each group must be obtained via a random sample from the population. Data in each group are normally distributed. Data values are continuous. The variances for the two independent groups are equal.
Which is the most important assumption in a t test?
Assumption 2: Normality A two sample t-test makes the assumption that both samples are approximately normally distributed. This is a crucial assumption because if the samples are not normally distributed then it isn’t valid to use the p-values from the test to draw conclusions about the differences between the samples.
When is the independent sample t test not trustworthy?
There is no relationship between the subjects in each sample. This means that: When this assumption is violated and the sample sizes for each group differ, the p value is not trustworthy. However, the Independent Samples t Test output also includes an approximate t statistic that is not based on assuming equal population variances.
When to use a two sample t test?
A two sample t-test is used to test whether or not the means of two populations are equal. 1. Independence: The observations in one sample are independent of the observations in the other sample. 2. Normality: Both samples are approximately normally distributed. 3. Homogeneity of Variances: Both samples have approximately the same variance.
Why do independent samples t test not assume equal variances?
Note that this form of the independent samples t test statistic does not assume equal variances. This is why both the denominator of the test statistic and the degrees of freedom of the critical value of t are different than the equal variances form of the test statistic.