As an example here is copypasta from the latest dietary guidelines:
Adults who are obese should change their eating and physical activity behaviors to prevent additional weight gain and/or promote weight loss. Adults who are overweight should not gain additional weight, and those with one or more CVD risk factors (e.g., hypertension and hyperlipidemia) should change their eating and physical activity behaviors to lose weight. To lose weight, most people need to reduce the number of calories they get from foods and beverages and increase their physical activity. For a weight loss of 1 to 11⁄2 pounds per week, daily intake should be reduced by 500 to 750 calories. Eating patterns that contain 1,200 to 1,500 calories each day can help most women lose weight safely, and eating patterns that contain 1,500 to 1,800 calories each day are suitable for most men for weight loss. In adults who are overweight or obese, if reduction in total calorie intake is achieved, a variety of eating patterns can produce weight loss, particularly in the first 6 months to 2 years; [9] however, more research is needed on the health implications of consuming these eating patterns long-term.
Straight wall to wall calories in calories out.
Skills: Learn both bayesian and frequentist statistics. E T Jaynes’s book, also Gelman’s Bayesian Data Analysis, and any solid frequentist textbook e.g. Goodman Teach Yourself Statistics 1972 edition. Also Judea Pearl Causality. Read the papers critiquing current methods (why most published research findings are false, the recent papers criticising the use of P values).
You will need calculus and linear algebra to get far but for reading the medical literature you can probably ignore measure theory.
Heuristics: Look at sponsorship, both for the study itself and for the researchers (speaking fees, sponsorship of other papers. This massively skews results.
Look for ideological or prior commitments by authors. This also massively skews results.
Look out for p hacking / garden of forking paths i.e. degrees of freedom that result in ‘significant’ results being claimed when this is not valid.
Understand the difference between statistical significance and practical significance. Understand how arbitrary the 5% threshold for statistical significance is. Understand that a result falling short of statistical significance may actually be evidence *for* an effect. No significant effect /= no effect, may mean probably is an effect.
Understand how little most medical people from GP to professors know about statistics and how often basic statistical errors occur in the literature (e.g. lack of statistical significant taken to be disproof as in the Vioxx debacle).
Read the methods section first. Don’t read the results part of the abstract or if you do, check that all the claims made are backed up by the body of the paper.
When reading meta-analyses look hard at the papers they are based on—you cannot make silk from sows ears. Be very wary of any study that has not been replicated by independent researchers.
Be aware of the extreme weaknesses of epidemiological and observational studies and be very sceptical of claims to have “controlled for” some variable. Such attempts are usually miserable failures, invalid and can make things actually worse. See Pearl’s book.