A researcher might find a statistically significant correlation between ice cream sales and crime rates, but this does not imply that dessert causes criminal behavior. Therefore, rejecting the null hypothesis means your data produced a p-value below this cutoff, indicating that the observed effect is unlikely to be a fluke of random sampling.
Why Flukes in Sampling Can Lead to Rejecting the Null Hypothesis
Such findings often point to lurking variables, such as hot weather, which influence both factors. Statistical significance is the probability that the observed results, or more extreme ones, would occur if the null hypothesis were true.
The strength of this evidence depends heavily on the study design, sample size, and the precision of the measurements used to gather the data. The Null Hypothesis and the Threshold of Significance To grasp the conclusion, one must first understand the premise.
Why Sampling Flukes Can Still Lead to Rejecting the Null Hypothesis
Furthermore, statistical significance is sensitive to sample size; with a large enough dataset, minuscule differences can become significant, while large, practically important differences might fail to reach significance due to high variability. This probability is measured by the p-value, and the conventional threshold for "significance" is set at 0.
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