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Brian Nosek @BrianNosek
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Many Labs 2: 28 findings, 60+ samples, ~7000 participants each study, 186 authors, 36 nations.

Successfully replicated 14 of 28

ML2 may be more important than Reproducibility Project: Psychology. Here’s why...

@michevianello @fredhasselman @raklein3
ML2 minimized boring reasons for failure. First, using original materials & Registered Reports all 28 replications met expert reviewed quality control standards. Failure to replicate not easily dismissed as replication incompetence.
Second, the total ML2 replication median sample size (n = 7157) was 64x original median sample size (n = 112). If there was an effect to detect, even a much smaller one, we would detect it. Ultimate estimates have very high precision.
Third, each original finding was replicated in >60 samples with labs from 36 nations and territories taking part. If the effect was easy to detect in some samples and not others, ML2 would find evidence for that.
Fourth, some original authors offered a priori hypotheses of moderating influences based on sample, task order, or other design features. Identifying them a priori provided an opportunity to test them with confidence.
The top line results of 50% overall replication success rate and effect sizes less than 1/2 of original studies are consistent with prior replication studies.
In sum, in ML2, the failures to replicate are not due to failure to meet expert review expectations, low power, heterogeneity of effects, hypothesized moderators, or task order. Example, Table 4 & Figure 3 show variation in effect size based on task order.
The main purpose of ML2 was to examine heterogeneity across sample & setting. Some heterogeneity was observed. It was mostly in large effects, not in weak effects. The notion that some “fragile” effects are highly sensitive to sample had no support here.
Heterogeneity was modest overall. For the most part, replicable effects replicated—some with varying strength. Unreplicable effects didn’t replicate, no matter where they were studied. Weaker effects replicated at ~ the rate expected for sample power.
We also explored whether effects varied substantially between WEIRD and less WEIRD cultures. A couple of cases showed meaningful differences, but most did not.

I find this Figure S2 to be particularly stunning.
We do not know generalizability of these findings. But, they provide a notable challenge to the “hidden moderators” hypothesis. Multiple moderators, plenty of power, little support. Not compelling to assert moderator explanation w/out testing it directly.
Also, we replicated evidence that surveys & markets can anticipate replication success. There are now 4 studies showing evidence for this.

Congratulations to project leads @raklein3 @michevianello @fredhasselman for completing this massive effort & to 180+ collaborators that were essential for project success. The work was comparable to 29 papers. The long-term impact will make it worth it.
Finally, all data, materials, and code are available on OSF for review and reuse

Test moderators. Do exploratory analysis on a subset, then apply model to holdout sample to maximize diagnosticity of stat inferences.
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