Allow me to clarify;

1. I didn't mean bias by the article, but bias in the study methodology. I haven't read the whitepaper yet, but I'm curious to see whether the researchers established a control group or a historical basis for their hypothesis. The article describes examples from recent history, and only briefly touched on historical examples:

   "By way of contrast, Gambetta and Hertog also explored which traits and disciplines applied to the opposite end of the political spectrum. Disgust seldom cropped up among those on the political left. And groups like the Baader-Meinhof Gang, in 1970s Germany, and Italy’s Red Brigades included few engineers but attracted plenty of social-science and humanities majors."

2. Statistical significance is much deeper than observed majority of results. Some of the first things any person with statistical training looks for are:

   • Control: did the researchers establish controls to rule out factors that could potentially manifest as statistical biases? This is tricky in social sciences because there are more variables than anyone could ever hope to control for — the best you can do as a researcher is to control as many variables as reasonably practical for a given analysis (and repeat the analysis with different combinations of controls if you can't control all of them at once).
   • Population size: are the observed samples from a large enough population? Since this is a natural experiment (i.e. you can't reproduce it in a lab setting), researchers have to wait for it to occur "in the wild" and therefore can't artificially grow their sampled population; this inherently limits the results.
   • Confidence: did the researchers establish a confidence interval of the results to support (or contradict) their hypothesis/es? This is difficult in social sciences because not all hypotheses can be supported with quantified values and are often best explained by interpreting the results using words.