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The p-value is not enough!- in Epidemiology
In my previous post, we looked at the overview of study designs, which try to establish the relation between exposure and outcome, in other words, whether an association exists or not!
Does exposure always mean the outcome?
Sometimes there may be spurious association because the exposure and outcome is not measured using robust study designs like RCT. For example, there is the high rates of sunburns and also high rates of ice cream consumption during hot seasons. But eating ice cream does not lead to sunburn but this third-factor heat is responsible for both high ice-cream eating and sunburns.
Third factor: Tertium quid – Epidemiology
The error in establishing an association may be due to pure chance, bias, or confounders.
Bias is the systematic deviation from the truth due to faulty study design while confounders confuse us by intermingling with the actual cause, check the video below to understand it better.
Statistics and Effect Size in Epidemiology: Must-have tools!
Amidst the confusion created by confounders and other factors not obviously causing the disease, epidemiologist use statistics and derive conclusions using p-value or probability which measure chance! One step ahead of the p-value is measuring the effect size, how big or large the difference between different exposure and the outcome. For example smoking case cancer 20 times more than red meat (only 0.2 times). Hence, the p-value is not enough!
#confoundingfactors #epidemiology #researchmethods