Kiran Kumar Gadhamsetty
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Kiran Kumar Gadhamsetty's post in Normality Tests was marked as the answerNormality test is a statistical hypothesis test used to determine if the sample has been drawn from a normally distributed population. Graphically, normal distribution resembles a bell with a symmetric distribution around the mean value. It is unimodal with mean, median & mode being equal. Commonly used tests for normality are Anderson Darling, Shapiro-Wilk, Kolmogorov-Smirnov & Chi Square.
Shapiro-Wilk test is sensitive to sample size. It is generally used for sample sizes less than 50. For larger sample sizes, the result is always statistically significant.
Kolmogorov-Smirnov is generally used for sample sizes greater than 50. It doesn't requires to know the underlying distribution of the population before running the test. It requires us to enter location, scale and shape parameters for running the test. It can't be used for discrete distributions.
Anderson Darling is a modification to Kolmogorov-Smirnov test with more weight to the tails than Kolmogorov-Smirnov test. It doesn't has a good power as that of Shapiro-Wilk test but offers better power than other tests.
Chi Square normality used when the sample has discrete set of data points. It is used when the expected value of number of sample of observations is greater than 5. The data must be randomly extracted and the variable of study is categorical.
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Kiran Kumar Gadhamsetty's post in Berkson's Paradox was marked as the answerBerkson's paradox describes a situation where the conclusion on correlation between two variables from a sample study is found to be against our intuition. This happens because of wrong sample selection.
For example, regular exercise keeps a person active. However, if the study is performed on hospitalized patients, the result can turn out to be counterintuitive.
As another example, regular investments in mutual funds provides a positive return. But, if the the study is performed on poorly performing funds, the result might be negative returns.
Sample selection needs to be done from a general population instead of a biased population to prevent occurrence of Berkson's paradox.