![]() ![]() One idea I've had is to simply not teach it at all or to not give it much weight, a position advocated by some (e.g. While I think this helps overall understanding of the course material in general, I don't think it's helped with this specific topic. I had to argue with the undergraduate administrator in our department to introduce mandatory sessions in the computer lab thinking that repeated demonstrations might be helpful (before I started teaching this course there was no computing involved). (I realize that it might be me and my teaching that's at issue here! However I think ignoring that uncomfortable possibility is reasonable to do since some students do seem to get it and overall everybody seems to do quite well.) From a poll I conducted last semester and from exam responses, I think that part of the difficulty is confusion between two related and similar sounding phrases (sampling distribution and sample distribution), so I've don't use the phrase "sample distribution" anymore, but surely this is something that, while confusing at first, is easily grasped with a little effort and anyway it can't explain the general confusion of the concept of a sampling distribution. It's difficult to explain what the precise issue is other than to say they just don't get it. The trouble students seem to have is with the application. I would guess that probably 60% leave the course with no to minimal understanding, about 25% understand the principle but not the connections to other concepts, and the remaining 15% fully understand. It's covered as the background for inference and follows a basic introduction to probability with which they don't seem to have trouble after some initial hiccups (and by basic, I mean basic - after all, many of these students have been self-selected into a specific course stream because they were trying to avoid anything with even a vague hint of "math"). It'll be the fifth time I've taught this course and one issue I've consistently had is that the students have really struggled with the notion of the sampling distribution. In September I'll be teaching an introductory statistics course for second year social science (mainly political science and sociology) students using The Basic Practice of Statistics by David Moore. The remaining sections of the chapter concern the sampling distributions of important statistics: the Sampling Distribution of the Mean, the Sampling Distribution of the Difference Between Means, the Sampling Distribution of r, and the Sampling Distribution of a Proportion.What successful strategies do you employ to teach the sampling distribution (of a sample mean, for example) at an introductory undergraduate level? The Central Limit Theorem (CLT) Demo is an interactive illustration of a very important and counter-intuitive characteristic of the sampling distribution of the mean. The Sample Size Demo allows you to investigate the effect of sample size on the sampling distribution of the mean. It is designed to make the abstract concept of sampling distributions more concrete. The Basic Demo is an interactive demonstration of sampling distributions. ![]() It also discusses how sampling distributions are used in inferential statistics. The introductory section defines the concept and gives an example for both a discrete and a continuous distribution. It is also a difficult concept because a sampling distribution is a theoretical distribution rather than an empirical distribution. The concept of a sampling distribution is perhaps the most basic concept in inferential statistics.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |