Erroneous inference based on a lack of preference within one group: Autism, mice, and the social approach task.
Stop trusting mouse social-approach claims that use within-group-only stats—ask for a between-group social preference index instead.
01Research in Context
What this study did
Hatfield et al. (2019) looked at how scientists test mouse social behavior. They used the Social Approach Task. This task checks if mice prefer to spend time with another mouse or with an empty cup.
Many labs only test each group on its own. They ask, "Does this one group prefer the mouse?" The authors say this is wrong. They show it creates false positives.
What they found
The paper shows that within-group-only stats (EWOCs) can claim a drug works when it does not. The fix is simple. Create a social preference index for every mouse. Then compare the index scores directly between groups.
This switch cuts false positives. It gives clearer answers about autism drugs tested in mice.
How this fits with other research
Hake (1982) warned that jumping from animal data to humans is risky. Hatfield et al. (2019) give a concrete fix for one part of that risk: bad stats in mouse social tests.
Jensen et al. (2013) push for tighter numbers in behavior work. The social-preference index is a live example of the rigor they call for.
Ching-Hsiang et al. (2009, 2010) also hack mouse hardware and software, but to help people with motor impairments. All four papers share the same theme: sharpen the measurement tool before you trust the result.
Why it matters
If you read mouse social data to pick autism interventions, demand the preference index and a between-group test. Refuse papers that hide behind EWOCs. You will skip drugs that look good in mice but fail in people, saving time and money for your clients.
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Join Free →Open the next mouse social-approach paper on your desk—check the stats section; if you see EWOCs, email the authors for the raw preference index and a between-group test.
02At a glance
03Original abstract
The Social Approach Task is commonly used to identify sociability deficits when modeling liability factors for autism spectrum disorder (ASD) in mice. It was developed to expand upon existing assays to examine distinct aspects of social behavior in rodents and has become a standard component of mouse ASD-relevant phenotyping pipelines. However, there is variability in the statistical analysis and interpretation of results from this task. A common analytical approach is to conduct within-group comparisons only, and then interpret a difference in significance levels as if it were a group difference, without any direct comparison. As an efficient shorthand, we named this approach EWOCs: Erroneous Within-group Only Comparisons. Here, we examined the prevalence of EWOCs and used simulations to test whether this approach could produce misleading inferences. Our review of Social Approach studies of high-confidence ASD genes revealed 45% of papers sampled used only this analytical approach. Through simulations, we then demonstrate how a lack of significant difference within one group often does not correspond to a significant difference between groups, and show this erroneous interpretation increases the rate of false positives up to 25%. Finally, we define a simple solution: use an index, like a social preference score, with direct statistical comparisons between groups to identify significant differences. We also provide power calculations to guide sample size in future studies. Overall, elimination of EWOCs and adoption of direct comparisons should result in more accurate, reliable, and reproducible data interpretations from the Social Approach Task across ASD liability models. Autism Res 2019, 12: 1171-1183. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The Social Approach Task is widely used to assess social behavior in mice and is frequently used in studies modeling autism. However, reviewing published studies showed nearly half do not use correct comparisons to interpret these data. Using simulated and original data, we argue the correct statistical approach is a direct comparison of scores between groups. This simple solution should reduce false positives and improve consistency of results across studies.
Autism research : official journal of the International Society for Autism Research, 2019 · doi:10.1016/j.neuron.2015.11.023