Which checks help avoid misinterpretation of statistics?

• A. Accept headline numbers
• B. Rely on p-values alone
• C. Verify sample size and methodology, examine confidence intervals, seek corroboration
• D. Ignore sample bias

Answer: (C)

Interpreting statistics responsibly comes down to understanding how the data were collected and how well the estimate holds up under scrutiny. Checking the sample size and the methods used to gather and analyze the data matters because these factors determine how representative and reliable the result is. A larger, well-designed study is generally more trustworthy than a small or poorly conducted one, since it reduces random error and bias. Looking at the confidence interval is essential because it shows the precision of the estimate; a narrow interval means we’re fairly sure about the effect size, while a wide interval signals a lot of uncertainty. Seeking corroboration, such as finding similar results in other studies or data sources, helps verify that the finding isn’t a one-off fluke and is more likely to reflect a real pattern.

This approach is better than simply accepting headline numbers, which can be misleading without context about what was measured and how. P-values alone don’t tell us how big or important an effect is, nor do they convey uncertainty or practical significance. Ignoring sample bias leads to distorted conclusions because biased data can systematically misrepresent reality.