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In this solo episode of Evidence in the Wild, I explore one of the most common pitfalls in interpreting data, confusing correlation with causation. Whether it's linking ice cream consumption to shark attacks, or assuming a program "works" based on surface-level trends, failing to account for confounding variables can lead to deeply flawed conclusions.
By Dr Joshua M StewartIn this solo episode of Evidence in the Wild, I explore one of the most common pitfalls in interpreting data, confusing correlation with causation. Whether it's linking ice cream consumption to shark attacks, or assuming a program "works" based on surface-level trends, failing to account for confounding variables can lead to deeply flawed conclusions.