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A non-normal distribution is any distribution of any kind other than normal. Most commonly in practice we find distributions are non-normal because they have a skew (a longer tail on the right or left side), though double-humped distributions and so on are also possible.
Sep 28, 2013 · The t-test and robustness to non-normality. The t-test is one of the most commonly used tests in statistics. The two-sample t-test allows us to test the null hypothesis that the population means of two groups are equal, based on samples from each of the two groups.
When the data follow a normal distribution, the normal probability plot will approximate a straight line. (A) Normal probability plot (skewed distribution). (B) Normal probability plot (normal distribution). However, a closer inspection of the data reveals that this conclusion is incorrect.
- Kristin L. Sainani
- 2012
Dealing with non-normal data. So far, the different t-tests you’ve practiced have all made an important assumption: normality. This is an assumption specifically about the way the data are distributed.
Jun 17, 2021 · If the data are incapable of becoming “normalized” by transforming the distribution to approximate a normal distribution, such as taking log 10 of all HIV viral load values, non-parametric tests should be applied to examine your data.
Spearman's correlation is a rank based correlation measure; it's non-parametric and does not rest upon an assumption of normality. The sampling distribution for Pearson's correlation does assume normality; in particular this means that although you can compute it, conclusions based on significance testing may not be sound.
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Apr 30, 2018 · The normal distribution is a continuous probability distribution that is symmetrical around its mean, most of the observations cluster around the central peak, and the probabilities for values further away from the mean taper off equally in both directions. Extreme values in both tails of the distribution are similarly unlikely.
