Contents
What to do with data that is not normally distributed?
Cases that are not solvable by rearranging the data. The data set is only a part of all the data and all the data outside the tolerance borders is filtered. On from left to right: the original data, without the data above the tolerance border, data without min max tolerance and only data above the upper tolerance.
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.
Is the sampling distribution of data in real life normal?
No data in real life is normal, a lot of them highly skewed, but that does NOT mean its sampling distribution is not normal. But many people do not know the difference so here’s a link to an animated explanation: http://onlinestatbook.com/stat_sim/sampling_dist/
Is the distribution of data a main objective?
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.
What should I do if my continuous variables are not normally distributed?
Extension A: What should I do if my continuous variables are not normally distributed? There are occasions where your continuous variables may not be normally distributed. This is often caused by ceiling or floor effects where data points gather at the extremes of the scale but it can occur for a great many reasons.
What happens when errors are not normally distributed?
When errors are not normally distributed, estimations are not normally distributed and we can no longer use p-values to decide if the coefficient is different from zero. In short, if the normality assumption of the errors is not met, we cannot draw a valid conclusion based on statistical inference in linear regression analysis.