Contents
How do you test if data is skewed?
The data are skewed but comparison of means still makes sense. A t-test will often work quite well in this situation, but watch out. The data are skewed and the most useful comparison may be to use a Wilcoxon-Mann-Whitney test. The data are skewed and are better analysed on a transformed (e.g. logarithmic) scale.
What effects do outliers have on data?
Outliers affect the mean value of the data but have little effect on the median or mode of a given set of data.
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.
Is the tail of a skewed distribution an outlier?
As Nick Cox points out in comments under this answer, a tail of many such points would be taken more as a indicator that a re-expression might be suitable than an indication of the need to regard the points as outliers. there is an assumption of a non-skewed distribution.
When is the t test valid on a skewed data?
The histogram of these means estimates the sampling distribution of the mean. The t-test is valid when this distribution is approximately Normal; the extent to which it deviates from Normality indicates the extent to which the Student t distribution will err.
When does your data violate one-sample t test assumptions?
If you find outliers in your data that are not due to correctable errors, you may wish to consult a statistician as to how to proceed. If the population from which the data were sampled is skewed, then the one-sample t test may incorrectly reject the null hypothesis that the population mean is the hypothesized value even when it is true.