- Climate Etc. recently carried several insightful posts about How we fool ourselves.
"...We have just completed a study for the National Association of Scholars [1] that took a deep dive looking at flawed statistical practices used in the field of environmental epidemiology. The study focused on air quality−health effect claims; more specifically PM2.5−health effect claims. However, the flawed practices apply to all aspects of risk factor−chronic disease research. The study also looked at how government bureaucrats use these claims to skew policy in favor of PM2.5 regulation and their own positions.
All that we discuss below is drawn from our study. Americans need to be aware that current statistical practices being used at the EPA for setting policy and regulations are flawed and obviously expensive. Viewers can download and read our study to decide the extent of the problem for themselves.
Academic researcher incentives reward exciting research with new positive (significant association) claims—but not reproducible research. This encourages epidemiologists – who are mainly academics – to wittingly or negligently use various flawed statistical practices to produce positive, but (we show) likely false, claims.
There are numerous key biases that epidemiologists continue to unintentionally (or intentionally) ignore in studies of air quality and health effects. This is done to make positive, but likely false, research claims. Some examples are:
- multiple testing and multiple modeling
- omitting predictors and confounders
- not controlling for residual confounding
- neglecting interactions among variables
- not properly testing model assumptions
- neglecting exposure uncertainties
- making unjustified interventional causal interpretation of regression coefficients..
3. How epidemiologists skew research...Read all!
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