NBPHE's Virtual Exam Review - Evidence-Based Approaches to Public Health

Improve your knowledge, skills, and confidence by joining us for a virtual CPH exam review course.

This is the seventh domain session, focused on Evidence-Based Approaches to Public Health, hosted by Anthony Dissen, EdD, MPH, MA, RDN, CPH.

This will be followed by three more sessions focused on the remaining exam domains for the Certification in Public Health (CPH), governed by the National Board of Public Health Examiners (NBPHE).

[mewolfe]

Please correct me if I'm wrong, but he has an error around minute 32. The definition he puts of Central Limit Theorem references MULTIPLE sample means forming normal distribution. But the question states one large sample. ONE large sample WOULD reflect the target pop (right-skewed). The MEANS of multiple large samples would be normally distributed, but this is referencing one sample group, one mean.
He is basically saying, even if the target population is skewed, the closer you get to sampling everyone (larger samples), the more normal the distribution gets. But if I sample 999 out of 1000 people, and the population is right-skewed, the sample distribution should also be right-skewed.
Again, please correct me if I'm wrong. I don't want to get it wrong on the test.

[dukedave]

1:11:32 He accidentally said "mean ages of comparisons" instead of "participants"...