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What test to use if the data is not normally distributed?
A non parametric test is one that doesn’t assume the data fits a specific distribution type. Non parametric tests include the Wilcoxon signed rank test, the Mann-Whitney U Test and the Kruskal-Wallis test.
Can you do at test if the data is not normally distributed?
For a t-test to be valid on a sample of smaller size, the population distribution would have to be approximately normal. The t-test is invalid for small samples from non-normal distributions, but it is valid for large samples from non-normal distributions.
Which is an example of a non-normal distribution?
Examples include: Weibull distribution, found with life data such as survival times of a product. Log-normal distribution, found with length data such as heights. Largest-extreme-value distribution, found with data such as the longest down-time each day.
What do you mean by normal distribution in Six Sigma?
Normally distributed data is a commonly misunderstood concept in Six Sigma. Some people believe that all data collected and used for analysis must be distributed normally. But normal distribution does not happen as often as people think, and it is not a main objective. Normal distribution is a means to an end, not the end itself.
Which is the first property of the normal distribution?
The First Known Property of the Normal Distribution says that: given random and independent samples of N observations each (taken from a normal distribution), the distribution of sample means is normal and unbiased (i.e., centered on the mean of the population), regardless of the size of N.
When does the distribution of sample mean approaches normality?
The long and formal answer to this question relies onCentral Limit Theorem which says that: given random and independent samples of N observations each, the distribution of sample means approaches normality as the size of increases, regardless of the shape of the population N distribution.