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.

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.

Too many degrees of freedom making minor differences appear misleadingly significant

Presence of confounding factors could mislead strength of association indicated by small p-value

Measurement error uniformly affecting all observations reducing accuracy but not directly impacting p-value

Single outlier might artificially inflate statistic driving down p-value

The observed sample does not meet pre-test criteria

The observed sample is not significantly different from what we would expect to see by chance alone

The observed sample is unlikely to have occurred by chance

The observed sample was biased

0.10

0.05

0.20

0.01

The evidence suggests that there is an association between chocolate consumption and cognitive function, but causation cannot be determined.

The p-value indicates that the sample data might not accurately represent the population regarding chocolate and cognition.

Eating chocolate causes improved cognitive function since the p-value is low.

Chocolate consumption has no effect on cognitive function as correlation does not imply causation.

Your result suggests that there's an error in your experimental design or data collection.

You would fail to reject the null hypothesis due to lack of evidence against it.

You conclude that your alternative hypothesis has an 85% chance of being true.

You would reject the null hypothesis because there's strong evidence against it.

To assess the statistical power of the test.

To determine the practical significance of the results.

To evaluate the reliability of the sample data.

To determine the statistical significance of the observed results.