I am currently trying to explore the determinants of management turnover using binary logit.
In robustness check, I have added more control variable (Industry) to see whether the same results hold. Surprisingly, almost all effects stay the same (economically and statistically), but a statistically insignificant predictor become significant. But, the sample size is reduced by almost 20% (From 1000 to ~800) due to missing industry info in some observations. I then run a regression with the same sample size of ~800 and leaving the newly included control variables. The result is the same with the robustness test. I suspect that this could be due to reduction in observations. Could you guide me on the explanation or provide a link on why would reducing sample sizes make the previously insignificant predictor significant?
Thanks a lot in advance.