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How do outliers affect the results of a t test?
Sheskin (2000) states that the presence of one or several outliers can have a substantial effect on both the mean and the variance of a distribution. If the variance is impacted by outliers test-statistics might not be reliable. However, the outliers in his study are fixed to a single, rather extreme value.
Are t tests affected by outliers?
Outliers are anomalous values in the data. The one-sample t statistic is based on the sample mean and the sample variance of the sample values, both of which are sensitive to outliers. (In other words, neither the sample mean nor the sample variance is resistant to outliers, and thus, neither is the t statistic.)
Do outliers affect the p-value?
2, with three outlier samples or less, power increases as effect size increases in the distribution-based p-value, the outlier robust t-statistic, and the outlier sum approaches, but not in the other two approaches for both normal and t-distributions. The distribution-based p-value approach performs best in most cases.
What does it mean when a t test is not significant?
This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).
Should outliers be removed before t-test?
Given the problems they can cause, you might think that it’s best to remove them from your data. But, that’s not always the case. Removing outliers is legitimate only for specific reasons. Consequently, excluding outliers can cause your results to become statistically significant.
What happens if your assumptions are violated?
Violations of the assumptions of your analysis impact your ability to trust your results and validly draw inferences about your results. You cannot provide an interpretation of the results based on the untransformed variable values.
What is a non-significant p-value?
A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance. A non-significant result, leading us not to reject the null hypothesis, is evidence that the null hypothesis is true. Non-significant results are a sign that the study has failed.
When to use outliers in a t test?
When the sample size gets up to 100, then things look better, though we are still quite a bit more conservative than necessary at the α = .01 level. Outliers mess up t-tests like nobodody’s business. You could have a sample size of 100000, and a single outlier of sufficient size could render your t-test completely invalid.
How are T-values, probabilities, and t-distributions work?
How t-Tests Work: t-Values, t-Distributions, and Probabilities. T-tests are statistical hypothesis tests that you use to analyze one or two sample means. Depending on the t-test that you use, you can compare a sample mean to a hypothesized value, the means of two independent samples, or the difference between paired samples.
When to use the t test in statistical analysis?
If the t-test rejects the null hypothesis (H₀: µ₁=µ₂), it indicates that the groups are highly probably different. This test should be implemented when the groups have 20–30 samples. If we want to examine more groups or larger sample sizes, there are other tests more accurate than t-tests such as z-test, chi-square test or f-test.
How does sample size affect the t-distribution?
t-Distributions and Sample Size The sample size for a t-test determines the degrees of freedom (DF) for that test, which specifies the t-distribution. The overall effect is that as the sample size decreases, the tails of the t-distribution become thicker.