Measurement error in a self-reported exposure posts 121–130

Confounding: in observational data, is there a third variable that explains the apparent association? In randomised data, randomisation should balance unknown confounders, though known confounders can be adjusted for.

Bias towards the null and bias away from the null: different criticisms have different directions. Differential dropout might bias away from null; conservative statistical analysis might bias toward null.

Multiple comparisons: if a paper reports many outcomes, the chance of a spurious association by random chance is real. Pre-specification of primary outcomes matters and secondary analyses are weaker evidence.

post #123 is right about the mechanism and I think understates the practical bit.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

Bias towards the null and bias away from the null: different criticisms have different directions. Differential dropout might bias away from null; conservative statistical analysis might bias toward null.

Multiple comparisons: if a paper reports many outcomes, the chance of a spurious association by random chance is real. Pre-specification of primary outcomes matters and secondary analyses are weaker evidence.

On post #123 — agreed on the reasoning, with one qualification.

Generalisability: do the inclusion/exclusion criteria narrow the population so much that results do not apply to real people asking about it? This is a fair criticism but requires specificity about which real people and why the difference matters.

post #127 answers the question as asked. The question underneath it is different.

Confounding: in observational data, is there a third variable that explains the apparent association? In randomised data, randomisation should balance unknown confounders, though known confounders can be adjusted for.

This follows post #127 rather than contradicting it.

Choosing the worst interpretation: "The confidence interval includes a harmful effect" is true if the CI goes from -1 to +5. But assuming the worst-case scenario is not how you use the evidence. The point estimate and the precision both matter.