Proportions

High variability ensures that any observed effects are practical significant even if they're not statistically significant according to this test result.

The experiment surely has flaws since high variability should always yield low p-values due to chance outcomes being likely outliers.

The data do not provide strong enough evidence against the null hypothesis at typical levels of $\alpha$ such as .05 or .01.

The null hypothesis can be accepted given that high variability and large p-values suggest consistency with hypothesized distribution under $H_0$.

Data supports or proves the alternative hypothesis absolutely.

Data is too varied to make any inference about hypotheses.

Data is consistent with both hypotheses and inconclusive.

Data is inconsistent with the null hypothesis.

Proof beyond any doubt that your experimental intervention was successful and effective.

Confirmation that other researchers will obtain exactly similar results when repeating your experiment.

A guarantee that there are no errors present in your experimental design or data collection methods.

Strong evidence against the null hypothesis, supporting consideration for its rejection.

The probability that the null hypothesis is false

The likelihood of making a Type II error in your test

The probability of observing your data, or more extreme, if the null hypothesis is true

The proportion of data points exceeding your test statistic value

The high P-value automatically invalidates all of your research findings.

You conclude that the null hypothesis cannot possibly be true given your results.

Your data collection process must have been compromised leading to this outcome.

You fail to reject the null hypothesis as there is insufficient evidence against it.

P-value is considered marginal and calls for caution in rejecting null hypothesis.

The null hypothesis should not be rejected given lack of strong evidence given by the p-value.

The p-value lacks reliability until cross-validated with another method or study.

The null hypothesis must be rejected given evidence against it provided by p-value.

0.10

0.05

0.20

0.01

Standard deviation

Variance

Interquartile range

Range

Mean absolute deviation

Standard deviation

Variance

Interquartile range

An increase in the effect size with other variables held constant.

A slight increase in the p-value due to rounding errors.

A decrease in the sample size while keeping other variables constant.

A reduction in variability within the sample data without changing other factors.