Big data in autism research: Methodological challenges and solutions.

Scior et al. (2023) wrote a theoretical paper. They looked at giant autism data sets. They asked how to keep those sets clean and useful.

The authors said big data can fool you. Without tight controls, noise looks like truth. They urged team science and strict design.

The paper found no new facts. Instead, it gave a warning. Huge samples mean huge mistakes if you skip the checks you use in small trials.

They listed fixes: pre-register your plan, share code, and invite statisticians, clinicians, and autistic people to the table.

LaPoint et al. (2025) extends this call. Two years later, they told autism intervention journals to demand trial registration and open protocols. The 2023 big-data plea became 2025 editorial policy.

Tromans et al. (2018) shows why the warning matters. Their survey of 529 RCTs found most had only 36 participants. Small studies stay small because teams work alone; big data promises scale, but K et al. say scale without rigor just makes bigger errors.

Swenson (2008) sang the same tune 15 years earlier. That paper warned autism genetics needed thousands of families and ancestry matching. Scior et al. (2023) echoes: large samples are fine only if you control confounds, whether genes or gigabytes.

If you sit on a data-sharing team, treat the upload like an RCT. Write the analysis plan before you touch the file. Lock it in a registry. Invite a BCBA, a statistician, and an autistic advocate to weekly calls. Big autism data can reveal new patterns, but only if you keep the small-study discipline that LaPoint and others keep demanding.