In multiple regression, all of the following circumstances suggest the need for larger sample sizes except:

Question

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Larger sample sizes are generally needed when stepwise regression is performed (A), when the estimated effect size is small (B), and when the number of predictors is large (C) to ensure sufficient statistical power and reliable estimates. Multicollinearity (D) refers to a situation where predictor variables are highly correlated with each other, which is a problem of the relationships among variables rather than sample size. Addressing multicollinearity typically involves removing or combining variables, not necessarily increasing the sample size.

In Multiple Regression, All of the Following Circumstances Suggest the Need