Ohio Assessments for Educators (OAE) Mathematics Practice Exam

A large sample size significantly increases the accuracy of inferential statistics because it reduces the margin of error in estimations of population parameters. When conducting statistical analyses, larger samples tend to yield more reliable and stable results. This is due to the Law of Large Numbers, which states that as the size of the sample increases, the sample mean approaches the population mean. Consequently, larger samples provide a better representation of the population, allowing for more precise confidence intervals and hypothesis testing.

Moreover, a large sample size tends to minimize the impact of outliers and anomalies that may skew results in smaller samples. With a greater number of observations, the effects of random variation are less influential, leading to more consistent and accurate conclusions. This enhances the ability to detect true effects and relationships within the data, making inferential statistics drawn from large samples more trustworthy.

In summary, by increasing the sample size, researchers can draw conclusions with greater certainty, making their inferences about the population more accurate.