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Common logical fallacies to watch out for when interpreting experimental res...
Method Help
Even experienced researchers can slip into common reasoning fallacies when interpreting experimental outputs, such as confusing correlation and causation. What frequent logical traps do you actively guard against in your own result interpretation work?
Benjamin Hill, Anthony Anderson and 15 others4 Comments-
Constantly remind yourself correlation does not automatically establish causal relationships.
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Beware over‑generalising findings beyond your actual study population or setup.
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Avoid treating absence‑of‑evidence as solid evidence‑of‑absence.
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Do not over‑weight statistically significant results while dismissing non‑significant ones.
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