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With high sample sizes a low p-value can be treated as evidence for lack of discrepancy. However, the opposite is not necessarily true: a high p-value, say 0.3, might be due to the low sensitivity of the test relative to the number of data points you have entered and the type of distribution. Since the null is that the data is normal, the alternative is that it is not normal, but note that these tests do not point to a particular alternative distribution. A low p-value is a stronger signal for a discrepancy and conventionally values under 0.05 are considered strong evidence for departure from normality (or IID, for some tests). The outcomes generated by our normality calculator consist of the p-value from each test and the test statistic (e.g. Interpreting the outcome of tests for normality erroneously recorded data, data from source later proven to be unreliable, etc.).
#NORMALITY TEST GRAPHPAD PRISM 6 FULL#
Normality tests should be run on the full data without removing any outliers, unless the reason for the outlier is known and its removal from the analysis as a whole can be readily justified (e.g. Using a statistical test designed under the assumption of Normal or NIID data when the data is not normal renders the statistical model inadequate and the results meaningless, regardless if one is dealing with experimental or observational data (regressions, correlations, etc.).
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Tests for normality like the Shapiro-Wilk are useful since many widely used statistical methods work under the assumption of a normally-distributed data and may require alteration in order to accommodate non-normal data. Separate tests for independence and heterogeneity can be performed to rule out those possibilities. The Null hypothesis can generally be stated as: "data can be modelled using the normal distribution", but since some normality tests also check if the data is independent and identically distributed (IID) a low p-value from these tests may be either due to a non-normal distribution or due to the IID assumption not holding. Normality tests can be based on the 3-rd and 4-th central moments (skewness and kurtosis), on regressions/correlations stemming from P-P and Q-Q plots or on distances defined using the empirical cumulative distribution functions (ecdf).