DABEST v2024.03.29
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Good afternoon!
The current time is Tue Mar 19 15:33:25 2024.
The unpaired mean difference between control and test is 0.5 [95%CI -0.0412, 1.0].
The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`
This is simply the mean of the control group subtracted from the mean of the test group.
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:26 2024.
The unpaired median difference between control and test is 0.5 [95%CI -0.0758, 0.991].
The p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.median_diff.statistical_tests`
This is the median difference between the control group and the test group.
If the comparison(s) are unpaired, median_diff is computed with the following equation:
Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead.
When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying ci_type = 'percentile' in .plot().
For detailed information, please refer to Issue 129.
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:27 2024.
The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0843, 0.976].
The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`
Cohen’s d is simply the mean of the control group subtracted from the mean of the test group.
If paired is None, then the comparison(s) are unpaired; otherwise the comparison(s) are paired.
If the comparison(s) are unpaired, Cohen’s d is computed with the following equation:
\[d = \frac{\overline{x}_{Test} - \overline{x}_{Control}} {\text{pooled standard deviation}}\]
For paired comparisons, Cohen’s d is given by
\[d = \frac{\overline{x}_{Test} - \overline{x}_{Control}} {\text{average standard deviation}}\]
where \(\overline{x}\) is the mean of the respective group of observations, \({Var}_{x}\) denotes the variance of that group,
\[\text{average standard deviation} = \sqrt{ \frac{{Var}_{control} + {Var}_{test}} {2}}\]
The sample variance (and standard deviation) uses N-1 degrees of freedoms. This is an application of Bessel’s correction, and yields the unbiased sample variance.
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:29 2024.
The unpaired Cohen's h between control and test is 0.0 [95%CI -0.613, 0.429].
The p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`
Cohen’s h uses the information of proportion in the control and test groups to calculate the distance between two proportions.
It can be used to describe the difference between two proportions as “small”, “medium”, or “large”.
It can be used to determine if the difference between two proportions is “meaningful”.
A directional Cohen’s h is computed with the following equation:
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:30 2024.
The unpaired Hedges' g between control and test is 0.465 [95%CI -0.0832, 0.963].
The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`
Hedges’ g is cohens_d corrected for bias via multiplication with the following correction factor:
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:41 2024.
The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0244, 0.533].
The p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`
Cliff’s delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.
where \(\#\) denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample.
Cliff’s delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from.
DABEST v2024.03.29
==================
Good afternoon!
The current time is Tue Mar 19 15:33:45 2024.
The unpaired deltas' g between W Placebo and M Placebo is 1.74 [95%CI 1.1, 2.31].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The unpaired deltas' g between W Drug and M Drug is 1.33 [95%CI 0.611, 1.96].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The deltas' g between Placebo and Drug is -0.651 [95%CI -1.59, 0.165].
The p-value of the two-sided permutation t-test is 0.0694, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing the effect size (or greater),
assuming the null hypothesis of zero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.delta_g.statistical_tests`
Deltas’ g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates hedges_g for delta-delta statistics.