What kind of test is the KS test?
The KS-test is a robust test that cares only about the relative distribution of the data. In the above case, use of the log scales just moved the important region so the user could see the distribution of the data. In a similar way we can examine the data in the first example:
How to calculate KS test statistic in Excel?
The function will either compute the KS test statistic from the actual data (sim=FALSE) or simulate a new dataset of the same size from a normal distribution with specified mean and sd. Then in either case will compute the test statistic comparing to a normal distribution with the same mean and sd as the sample (original or simulated).
Is the K-S test based on the maximum distance?
The graph below is a plot of the empirical distribution function with a normal cumulative distribution function for 100 normal random numbers. The K-S test is based on the maximum distance between these two curves. Characteristics and Limitations of the K-S Test
Which is better KS test or ml test?
If we need to decide for Student-T data with df = 2 via KS test whether the data could be normal or not, then a ML estimate based on H 0 (data is normal, so using the standard deviation for scale) would give much larger KS distance, than a fit with minimum KS.
What is the maximum deviation of the KS test?
The KS-test uses the maximum vertical deviation between the two curves as the statistic D. In this case the maximum deviation occurs near x =1 and has D =.45. (The fraction of the treatment group that is less then one is 0.2 (4 out of the 20 values); the fraction of the control group that is less than one is 0.65 (13 out of the 20 values).
Why is the t-test called a robust test?
Thus the t-test is called a “robust” test, since it continues to work well in situations different from those narrow situations for which it was created. This is important because users of statistical tests often do not know if their dataset meets the criteria intended by the creator of the statistical test.