Good afternoon, could provide me with an explanation to the paragraph below. Identifying information that supports or contradicts the information and references to support your explanation.

You have noted that "estimates with a large sample size are more likely to be reliable than estimates with a small sample size," which is proper if we take a small sample, we don't have much information on which to base our inference. Small samples will vary more from each other. It's given that there is more variation due to sampling, or sampling error, with a small sample. However, in larger samples, the effect of a few unusual values is evened out by the other values in the sample. Larger samples will be more similar to each other, and the effect of sampling error is reduced with larger samples. When we take a large sample, we have more information and can be more certain about our estimate. All in all, the width of a confidence interval depends on two things, the variation within the population of interest and the sample size. If all the values in the population were almost the same, then our sample would also have slight variation.

Reference:

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social Statistics for a diverse society. SAGE Publications, 7.

Salkind, N. J. & Frey, B. B. (2019).Statistics for people who (think they) hate statistics(7th ed.). SAGE Publications, Inc.