Final answer:
To correct standard errors in OLS estimates with small sample sizes, researchers apply a method based on the Student's t-distribution, which provides more accurate confidence intervals for estimated parameters.
Step-by-step explanation:
Researchers seeking to improve OLS (ordinary least squares) estimates when dealing with small sample sizes often apply a correction for the standard errors, which adjusts the confidence intervals for the estimated parameters. This correction accounts for the fact that, with smaller samples, we cannot reliably use the sample standard deviation as a direct estimate for the population standard deviation (o). William S. Gosset addressed this issue by developing the Student's t-distribution, which provides a foundation for the creation of more accurate intervals when the sample size is limited and the population standard deviation is unknown.
When constructing confidence intervals with the corrected standard errors, they have the form of the point estimate ± the margin of error. The margin of error encapsulates the uncertainty in the estimate and is adjusted based on the level of confidence desired. Using the Student's t-distribution for the calculation of the margin of error allows for more reliable estimation of the true population parameters from small sample sizes.