What should a researcher conclude if the p-value is .19 and the alpha value is set at ≤ .05?

In hypothesis testing, the p-value is a measure that helps determine the strength of the evidence against the null hypothesis. When the p-value is greater than the alpha level, which is commonly set at 0.05, it indicates that the observed results are not statistically significant. In this scenario, with a p-value of .19, it signifies that there is a 19% probability of observing the data if the null hypothesis is true.

When a researcher finds that the p-value exceeds the alpha level, it suggests that the evidence is insufficient to conclude that there is a real effect or difference present. Thus, the researcher would infer that any observed difference could likely be attributed to chance or random error rather than a true effect in the population. Therefore, concluding that the experimental result is likely due to chance or error is accurate and aligns with the principles of hypothesis testing.