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How do you interpret a KSL or Kolmogorov-Smirnov-Lilliefors test?
If the test result is significant (Lower than or equal to 0.05) = The data is not normally distributed... If the test result is not significant (Higher than 0.05) = The data is normally distributed... This synchronize with the Statistical Hypothesis Assumption (Ho and Ha) Ho means "Nothing Happen" and Ha means "Something Happen" then for KSL and Shapiro Wilk test of normality assumption also.... If the test result reject Ha and accept Ho means "NOTHING HAPPEN" to data or the data is normally distributed but if the result reject Ho and accept Ha means "SOMETHING HAPPEN" to data or in this case the data is NOT normally distributed. Dr.Tanarat Thiengkamol (send2nude@gmail.com)
How do you know whether data requires you to use a parametric or non parametric test?
Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.
When doing a t-test what does normal distributed in population mean?
It means that the random variable of interest is Normally distributed and so the t-distribution is an appropriate distribution for the test rather than just an approximation.
What are examples of parametric and nonparametric statistical tests?
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
How do one determine when a data is normally distributed?
Use the Kolmogorov Smirnoff goodness-of-fit test. A normal distribution is a bell shaped curve, which is nearly symmetrica. It looks like an upside down bell. It can be squished low (platykurtic) or pulled high and skinny (leptokurtic) but it is still bell shaped and symmetrical. A mathematical test is to use the pearson's skew. If the pearson's skew is between 0 and 0.49, then the data is a non-problematic or normally distributed. If it is greater than 0.50, then it is not a normal distribution so one cannot treat it as such. The pearson's skew equation is skew p= (3 (mean - median)) / (SD(x) SD(y))
