Why would something not be normally distributed?
Reasons for the Non Normal Distribution Many data sets naturally fit a non normal model. For example, the number of accidents tends to fit a Poisson distribution and lifetimes of products usually fit a Weibull distribution. Outliers can cause your data the become skewed. The mean is especially sensitive to outliers.
Can you run at test on non-normal data?
The t-test is invalid for small samples from non-normal distributions, but it is valid for large samples from non-normal distributions. As Michael notes below, sample size needed for the distribution of means to approximate normality depends on the degree of non-normality of the population.
Why normal distribution is not a good model?
Give a reason why a normal distribution, with this mean and standard deviation, would not give a good approximation to the distribution of marks. My answer: Since the standard deviation is quite large (=15.2), the normal curve will disperse wildly. Hence, it is not a good approximation.
Why does t-test need normal distribution?
The purpose of the t-test is to compare certain characteristics representing groups, and the mean values become representative when the population has a normal distribution. This is the reason why satisfaction of the normality assumption is essential in the t-test.
What does it mean if residuals are not random?
Non-random patterns in your residuals signify that your variables are missing something. Importantly, appreciate that if you do see unwanted patterns in your residual plots, it actually represents a chance to improve your model because there is something more that your independent variables can explain.
What are the limitations of normal distribution?
One of the disadvantages of using the normal distribution for reliability calculations is the fact that the normal distribution starts at negative infinity. This can result in negative values for some of the results.