Small Samples
Sample Size of Group 1 (n1): 5; Sample Size of Group 2 (n2): 5; Sum of Ranks for Group 1: 30
U Statistic for Group 1: 10; U Statistic for Group 2: 15; U Statistic (smaller of U1 and U2): 10; Z-Score (for large samples): -0.5222329679
Performs a non-parametric test to compare differences between two independent groups when the assumption of normality is violated. It calculates the U statistic and associated p-value.
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Enter Sample Size of Group 1 (n1), Sample Size of Group 2 (n2), and Sum of Ranks for Group 1 in the units shown.
Read U Statistic for Group 1, U Statistic for Group 2, U Statistic (smaller of U1 and U2), and Z-Score (for large samples) in the results panel. Results update as you change the inputs.
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See the inputs and their calculated results.
Sample Size of Group 1 (n1): 5; Sample Size of Group 2 (n2): 5; Sum of Ranks for Group 1: 30
U Statistic for Group 1: 10; U Statistic for Group 2: 15; U Statistic (smaller of U1 and U2): 10; Z-Score (for large samples): -0.5222329679
Sample Size of Group 1 (n1): 10; Sample Size of Group 2 (n2): 10; Sum of Ranks for Group 1: 120
U Statistic for Group 1: 35; U Statistic for Group 2: 65; U Statistic (smaller of U1 and U2): 35; Z-Score (for large samples): -1.133893419
Sample Size of Group 1 (n1): 15; Sample Size of Group 2 (n2): 10; Sum of Ranks for Group 1: 180
U Statistic for Group 1: 90; U Statistic for Group 2: 60; U Statistic (smaller of U1 and U2): 60; Z-Score (for large samples): -0.8320502943
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Performs a t-test (one-sample, two-sample, or paired) to compare means and determine statistical significance.
Finds the Z-score corresponding to a given cumulative probability (percentile) on a normal distribution.
Calculates the Chi-Square statistic and p-value for goodness-of-fit or test of independence. It requires observed and expected frequencies or values.
Identifies potential outliers in a dataset using the IQR * 1.5 rule (calculating fences).