Key takeaways:
- Randomization in studies can lead to misinterpretation of results due to overreliance on statistical significance from small, noisy samples.
- The misunderstanding between statistical significance and proven causality is common, highlighting the limitations of single studies in proving causality.
- Strategies like stratification by pre-experiment data can limit bad randomizations but are underused due to potential interference with p-hacking.
# Discussion Overview
- Hacker News users engaged in a thoughtful discussion on the pitfalls and misconceptions surrounding the use of randomization in scientific studies, triggered by a post from Columbia University.
# Misinterpretations of Randomization
- Randomization doesn't inherently worsen a study but can lead to overly confident interpretations of the results.
"Randomization doesn’t make a study worse. What it can do is give researchers and consumers of researchers an inappropriately warm and cozy feeling, leading them to not look at serious problems of interpretation of the results of the study."
# Statistical Significance vs. Causality
- There's a widespread misunderstanding that statistically significant results from a single study can prove causality.
- Meta-analysis is suggested as a closer approach to truth, although the limitations of single Randomized Controlled Trials (RCTs) in establishing causality are noted.
# The Importance of a Strong Model
- The discussion emphasizes the necessity of a strong underlying model to correctly interpret correlations and avoid confounding variables.
- Examples highlight how correlations can be misleading without a solid model to establish the direction and nature of the relationship.
# Addressing Bad Randomizations
- Techniques such as stratification by pre-experiment data are recommended to mitigate poor randomization effects, although their use may be limited by the desire to avoid hindering p-hacking.
# Critique of Misleading Professionalism
- Concerns are raised about the practice of adding superficial layers of professionalism, like white papers, to lend unmerited credibility to studies, drawing a parallel to societal issues with surface-level professionalism.
This summary encapsulates a nuanced discussion on Hacker News about the complexities and challenges of interpreting the results of randomized studies, the misunderstanding between statistical significance and causality, and the importance of a strong model in research.
source: Randomization in such studies is arguably a negative in practice | Hacker News