Interpreting the results of inferential statistics involves understanding the significance, effect sizes, and potential biases. Here are key points to consider:
Statistical Significance: A p-value less than 0.05 typically indicates that the result is statistically significant, meaning it is unlikely to have occurred by chance. Effect Size: Measures such as odds ratios or relative risks provide information on the strength of the association or effect. Confidence Intervals: Narrow confidence intervals indicate precise estimates, while wide intervals suggest more variability and less certainty. Bias and Confounding: Researchers must account for potential biases and confounding variables that could affect the results.
