Which is not a sign that the results in a statistical test is significant?

The reasoning behind why the choice indicating that the result matches initial expectations for the test’s outcome is not a sign of statistical significance is rooted in the definition and understanding of what statistical significance entails. Statistical significance primarily refers to whether the results of a study or experiment are likely due to chance, based on the data collected.

When a result matches initial expectations, it does not provide evidence of the statistical properties of the data. It is merely an observation that aligns with prior beliefs or hypotheses, which can lead to confirmation bias. Statistical significance, on the other hand, is determined by objective measures: when the p-value is less than the predetermined significance level, this indicates that the evidence against the null hypothesis is strong enough to infer that the observed effect is likely not due to random variation. Similarly, when a confidence interval does not include zero, it indicates that there is a statistically significant effect at a certain confidence level, further affirming the findings. Furthermore, a confidence level set above 95% influences the threshold for determining significance, but does not affect whether results are significant in a valid test. Thus, while matching expectations can feel validating, it does not serve as a reliable indicator of statistical significance in a robust analysis.