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The more flexible a scientific field is in its definitions, experimental designs, analytic modes, and outcomes, the less likely that research conclusions are reliable. Biology, psychology, sociology, and economics are exemplary fields where empirical problems loom large.
Getting good data is the 1st hurdle, and is where many studies falter, often without the acknowledgement of those involved. The 2013 study aimed at cosmic inflation is an egregious example – caught only because the study was heavily scrutinized (which is itself quite unusual), and the flaws in data aggregation method so obvious.
Once amassed, data is then subject to statistical interpretation. If a study has not already been invalidated for lack of decent data, it is in this step that results readily go awry.
Even when performed properly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless scientific conclusions that make the news are erroneous.
A lot of scientists don’t understand statistics. And they don’t understand statistics because the statistics don’t make sense. ~ American epidemiologist Steven Goodman
