Quiz-3

Short answer + common missed question solutions

Question 3: The Central Limit Theorem states that we always need to have at least a sample size of at least n = 30 for the sampling distribution of a sample statistic to be approximately normal.

False. The type of variable(s) we are working with dictate what sample size assumption we need to check. For quantitative variables, we need to have > 30 observations. However, for categorical variables, we need to make sure the success failure condition is met. This can be done with a sample size of < 30.

Question 5: In your own words, describe the concept of sampling variability. If you use outside resources, please cite them here.

Sampling variability is the differences that occurs when sample data are taken from a larger population multiple times, leading to different results. For example, if one researcher goes out, samples 100 NC State students, and calculates their mean height… we would not expect another researcher that samples 100 NC State students to get the exact same mean height

Question 10: A researcher interested in studying NC Students stands outside of SAS Hall on a Tuesday and surveys the first 50 people they see.

Is this considered random sampling? Justify your response.

To answer this question, we need to define what random sampling is. We define random sampling as everyone in the entire population having an equal chance of being selected. From the prompt, we know the population of interest is NC State students. The probability of sampling students is not equal across all students if your sample is collected outside of SAS Hall on a Tuesday. Students that have class on Tuesday compared to Monday have a much higher probability of being selected. Further, students with class in SAS hall have a higher probability of being selected than those who do not.