We’re all familiar with statistics as a foundational course (or series of courses) for any upcoming undergraduate researcher, be it hard or soft sciences. Its importance and implications are beyond the sciences. Indeed, statistics comes crucially into play when making sense and significance of any observations made, whether it be in policy, business, sports, or any of the specializations in STEM as a whole.
But, for someone familiar with the fast-moving, deadline-rich path of academia, statistics is often taught quickly and out of context, giving useful background knowledge that is not truly cemented in, or with real world application.
Knowledge of statistical methods is necessary but not sufficient to be grounded in statistical thinking applied where it really matters: the real world. Most of the time, statistical training does not blossom and show it’s fruits until a researcher is using the statistical program R and analyzing their own data towards their thesis, and nearly all researchers encounter new statistical obstacles as their research progresses.
Statistical Rigor is Suffering in the Biomedical Sciences
With hypercompetition in the biomedical sciences and an over-reliance on statistical significance due in part to journals and the media prioritizing flashy, significant results over true findings, statistical rigor as a whole is suffering in the biomedical sciences. The booming field of meta-research is a promising antidote to the systemic problems encountered in the biomedical sciences, and a central concern in meta-research is addressing statistical illiteracy, more specifically, shedding light on the ill-founded reliance on statistical methods that introduce sloppiness and bias into research results.
An ABmR Statistics Series
This statistics series will be an attempt to ground upcoming researchers in solid statistical literacy (from a broader philosophy of science as well as a hard, quantitative statistics perspective) while connecting the information to real science being done. The goal is to develop an intuition and understanding of the basics of statistics used in the biomedical sciences and, ultimately, to sharpen one’s statistical toolkit to better equip the upcoming undergraduate researcher with the means to evaluate and judge research findings in their own research and in the field as a whole.