One big problem with this is that scientists don't report all of their findings. The result is a number that is closer to reality. This is similar to how poll aggregators theoretically remove noise by combining the results of many different polls (unless of course pollsters are systematically biased). We can aggregate the reported effect size across many papers in order to determine an even "truer" effect, one that removes experimenter bias and noise. Many scientists conduct similar experiments, and report the effect size they found in their papers (e.g., when Chris drinks 2 cups of coffee, he cleans his kitchen an average of 1 hour longer). One way to do this is to conduct some experimental manipulation (e.g., drinking variable amounts of coffee), and measuring its effect on a dependent variable (e.g., how many minutes I spend cleaning my kitchen). The (theoretical) goal of science is to observe and accurately describe various phenomena in nature. (note, all of the plots are taken from the excellent paper The Rules of the Game of Psychological Science, though funnel plots date back at least to the book Summing Up by Light and Lillemer)īefore diving into the guts of funnel plots, we first need to talk about experiments and effect sizes. # Some quick imports we'll use later import numpy as np from scipy.stats import distributions from matplotlib import pyplot as plt from import interact from IPython.display import Image % matplotlib inline Read on below to learn about why funnel plots are a great way to visualize the problems our publishing system faces. This is an attempt to make these thoughts a little more digestible, discoverable, and useful. However, I've found that these insights often come buried within relatively dense papers that are themselves hidden behind subscription journal paywalls.
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People have spoken at length about scientific principles and how to improve them for quite a long time. I'm not really dredging up anything new here.
Whether they dedicate all of their research to these "meta science" topics, or simply treat this as a part of their scientific duty on top of their domain-specific work, their work represents a crucial step in reforming our scientific culture. While it's easy to make one-off complaints to one another about how "science is broken" without really diving into the details, it's important learn about how it's broken, or at least how we could assess something like this.įortunately, there are a lot of great researchers out there who are studying these very issues. In the next few months, I'll try to take some time to talk about the things I learn as I make my way through this literature.
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