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What is the p-value in an independent t test?
(2-tailed) – The p-value is the two-tailed probability computed using the t distribution. It is the probability of observing a t-value of equal or greater absolute value under the null hypothesis. For a one-tailed test, halve this probability.
How do you know if an independent t test is significant?
We are primarily concerned with the significance value – if it is greater than 0.05 (i.e., p > . 05), our group variances can be treated as equal. However, if p < 0.05, we have unequal variances and we have violated the assumption of homogeneity of variances.
What is the true p value of a t-test?
The true p-value is 0.15264, which is pretty close to our estimated p-value of 0.15. We saw in this post that it’s possible to estimate the p-value of a t-test by hand using the t-Distribution table.
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.
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.
When to use Levene’s test or independent samples t test?
SPSS conveniently includes a test for the homogeneity of variance, called Levene’s Test, whenever you run an independent samples t test. This implies that if we reject the null hypothesis of Levene’s Test, it suggests that the variances of the two groups are not equal; i.e., that the homogeneity of variances assumption is violated.