One of the biggest issues I see people make with evidence appraisal is the idea that you have to have a yes/no answer That's not how evidence works It's ALMOST ALWAYS shades of grey
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It seems like there should always be a dichotomy there - either a study is right or wrong, surely? The reality is that unless you look at the extreme ends of the spectrum, this is rarely true
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So, for example, unless a trial enrolls a truly tiny number of people - less than 20, say - the fact that they had an inadequate sample size doesn't necessarily mean the research is worthless
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Similarly, industry funding is a known bias, but it may not impact the results that much This is why interpretation of study design takes some expertise
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When epidemiologists review studies, they usually do what's called "evidence grading" This is a formal assessment of the potential biases that the study has, and a judgement on how you should interpret the conclusions
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You might see someone say that the study has a very high risk of bias This means that there are many places in the study where outside influences may have crept in and caused the conclusions to be flawed
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Similarly, a very low risk of bias means that the research was done impeccably and the conclusions are likely robust
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BUT NEITHER OF THESE GIVES YOU A YES OR NO ANSWER
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They are simply judgments on how likely it is that the conclusions are correct. But it's a gradation rather than a dichotomy
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The fact is, most studies are flawed in some way. Even the very best may not be easily usable for many reasons But a potentially biased study can still provide invaluable information
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Some examples of what biased studies have shown us: - smoking causes cancer - seatbelts save lives - asbestos causes mesothelioma - iodine deficiencies cause issues
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