In much the same way, men almost always overestimate their height and everyone thinks they exercise more than they do Since these are known biases, we can easily correct for them
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The best part about issues like these is that they can actually make a study stronger! This sounds counterintuitive, but it is often true
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This is because issues that affect all groups in a study tend to bias the results towards the null hypothesis - i.e. they tend to make any results you DO find more significant
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For example: if you measured calorie intake across 5 groups, and the 'true' result is 1. 1800 2. 2000 3. 2200 4. 2400 5. 2600
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Now, generally speaking people underestimate their calorie intake. Let's say using this food questionnaire it is by 20% So the values you OBSERVE are: 1. 1440 2. 1600 3. 1760 4. 1920 5. 2080
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In our first example - the TRUE value - the difference between each group is 200 In our second example - the OBSERVED value - the difference is only 160
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So what's happened here is that the issues with our study have changed the results, thus biasing towards the null hypothesis
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If we still manage to find a difference, it means that the difference is even more compelling An issue with our measurement is now actually making our results even more impressive!
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This is one of many reasons why epidemiology can be pretty confusing End thread
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Lolz