Causes of Misleading Statistics and Research Results Irreproducibility: A Concise Review

TL;DR

This paper reviews common statistical fallacies and bad practices that lead to unreproducible and misleading research results, emphasizing the importance of open science for improving research integrity.

Contribution

It provides a concise overview of causes behind statistical misuse and research irreproducibility, highlighting the need for adopting open science practices.

Findings

Statistical fallacies significantly impact research reproducibility.

Proper practical solutions to statistical issues have been known for years.

Open science can improve the entire research process.

Abstract

Bad statistics make research papers unreproducible and misleading. For the most part, the reasons for such misusage of numerical data have been found and addressed years ago by experts and proper practical solutions have been presented instead. Yet, we still see numerous instances of statistical fallacies in modern researches which without a doubt play a significant role in the research reproducibility crisis. In this paper, we review different bad practices that impact the research process from its beginning to its very end. Additionally, we briefly propose open science as a universal methodology that can facilitate the entire research life cycle.