We all know what psychologists did before 2012. The name of the game was to get significant results that could be sold to a journal for publication. Some did it with more power and some did it with less power, but everybody did it.
In the beginning of the 2010s it became obvious that this was a flawed way to do science. Bem (2011) used this anything-goes to get significance approach to publish 9 significant demonstration of a phenomenon that does not exist: mental time-travel. The cat was out of the bag. There were only two questions. How many other findings were unreal and how would psychologists respond to the credibility crisis.
D. Steve Lindsay responded to the crisis by helping to implement tighter standards and to enforce these standards as editor of Psychological Science. As a result, Psychological Science has published more credible results over the past five years. At the end of his editorial term, Linday published a gutsy and honest account of his journey towards a better and more open psychological science. It starts with his own realization that his research practices were suboptimal.
Early in 2012, Geoff Cumming blew my mind with a talk that led me to realize that I had been conducting underpowered experiments for decades. In some lines of research in my lab, a predicted effect would come booming through in one experiment but melt away in the next.
My students and I kept trying to find conditions that yielded consistent statistical significance—tweaking items, instructions, exclusion rules—but we sometimes eventually threw in the towel because results were maddeningly inconsistent. For example, a chapter by Lindsay
and Kantner (2011) reported 16 experiments with an on-again/off-again effect of feedback on recognition memory. Cumming’s talk explained that p values are very noisy. Moreover, when between-subjects designs are used to study small- to medium-sized effects, statistical
tests often yield nonsignificant outcomes (sometimes with huge p values) unless samples are very large.
Hard on the heels of Cumming’s talk, I read Simmons, Nelson, and Simonsohn’s (2011) “False-Positive Psychology” article, published in Psychological Science. Then I gobbled up several articles and blog posts on misuses of null-hypothesis significance testing (NHST). The
authors of these works make a convincing case that hypothesizing after the results are known (HARKing; Kerr, 1998) and other forms of “p hacking” (post hoc exclusions, transformations, addition of moderators, optional stopping, publication bias, etc.) are deeply problematic. Such practices are common in some areas of scientific psychology, as well as in some other life
sciences. These practices sometimes give rise to mistaken beliefs in effects that really do not exist. Combined with publication bias, they often lead to exaggerated estimates
of the sizes of real but small effects.
This quote is exceptional because few psychologists have openly talked about their research practices before (or after) 2012. It is an open secrete that questionable research practices were widely used and anonymous surveys support this (John et al., 2012), but nobody likes to talk about it. Lindsay’s frank account is an honorable exception in the spirit of true leaders who confront mistakes head on, just like a Nobel laureate who recently retracted a Science article (Frances Arnold).
1. Acknowledge your mistakes.
2. Learn from your mistakes.
3. Teach others from your mistakes.
4. Move beyond your mistakes.
Lindsay’s acknowledgement also makes it possible to examine what these research practices look like when we examine published results, and to see whether this pattern changes in response to awareness that certain practices were questionable.
So, I z-curved Lindsay’s published results from 1998 to 2012. The graph shows some evidence of QRPs, in that the model assumes more non-significant results (grey line from 0 to 1.96) than are actually observed (histogram of non-significant results). This is confirmed by a comparison of the observed discovery rate (70% of published results are significant) and the expected discovery rate (44%). However, the confidence intervals overlap. So this test of bias is not significant.
The replication rate is estimated to be 77%. This means that there is a 77% probability that repeating a test with a new sample (of equal size) would produce a significant result again. Even for just significant results (z = 2 to 2.5), the estimated replicability is still 45%. I have seen much worse results.
Nevertheless, it is interesting to see whether things improved. First of all, being editor of Psychological Science is full-time job. Thus, output has decreased. Maybe research also slowed down because studies were conducted with more care. I don’t know. I just know that there are very few statistics to examine.
Although the small sample size of tests makes results somewhat uncertain, the graph shows some changes in research practices. Replicability increased further to 88% and there is no loner a discrepancy between observed and expected discovery rate.
If psychology as a whole had responded like D.S. Lindsay it would be in a good position to start the new decade. The problem is that this response is an exception rather than the rule and some areas of psychology and some individual researchers have not changed at all since 2012. This is unfortunate because questionable research practices hurt psychology, especially when undergraduates and the wider public learn more and more how untrustworthy psychological science has been and often still us. Hopefully, reforms will come sooner than later or we may have to sing a swan song for psychological science.