What should I do if the bootstrapping test p value is not significant?

When the Bootstrap test p-value is not significant, next steps include: assessing sample size, examining data distribution, exploring alternative hypotheses, examining actual differences, considering other tests, seeking expert opinion, and interpreting results with caution.

Bootstrap test p-value is not significant: Next steps

The Bootstrap test is a resampling technique that uses to estimate the accuracy of statistical inferences. When the p-value for a Bootstrap test is not significant, it means that the study data do not provide enough evidence to reject the null hypothesis that there is no difference between the two groups.

Next steps:

1. Evaluate sample size:

Low sample size may result in insufficient statistical power, thereby Increase the likelihood of non-significant results. Consider increasing the sample size to increase the power of the test.

2. Check the data distribution:

Ensure that the data distribution meets the assumptions of the Bootstrap test. If your data are highly skewed or have many outliers, you may need to transform your data or use nonparametric tests.

3. Explore Alternative Hypotheses:

Consider alternative hypotheses, that is, there is a difference between the two groups, but it may not be the difference originally proposed. Exploratory analysis of data was performed to identify potential patterns of differences.

4. Examine the actual difference:

Even if the p-value is not significant, there may be an actual difference. Calculate an effect size (e.g., Cohen's d) to quantify the actual size of the difference between the two groups.

5. Consider other tests:

Try other non-parametric tests, such as rank sum tests, which require fewer assumptions. These tests may be more robust to data with non-normal distributions or many outliers.

6. Seek Expert Opinion:

Consult a statistical expert or domain expert to discuss the implications of non-significant results and next steps. They can provide insight and advice to help make informed decisions.

7. Interpret with caution:

Avoid over-interpreting non-significant results. Instead, discuss the limitations of the study and suggest areas for further research.

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