Consider we are collecting samples of sample size n=2, now what does the central limit theorem says? Collecting the samples of sample size n =2 a million times(number of samples), and their sample means would form normal distribution? Or is it as the sample size n increases it tends to be normal?

In law of large numbers, one of the assumption is sample size is large enough, again is it sample size n or the number of samples.

Also, collecting sample of sample size n=2 a hundred times independently is the same as collecting sample of sample size n=1 a two hundred times. Whatâ€™s the difference and how are sampling done in real time?

Thanks

Hey @karthikeyan_S2,

Welcome, and we are glad that you could become a part of our community

Thanks for creating this thread. I was actually for something like this, since I myself had some confusions in understanding the lecture videos â€śLaw of Large Numbersâ€ť and â€śCentral Limit Theorem - Discrete Random Variableâ€ť. In my opinion, the videos only serve the purpose of confusing the learners (*be they have some prior knowledge, or be they beginners*).

Letâ€™s begin with discussing the discrepancies in Week 3 (*Lecture 1*), which I have come across till now (*Central Limit Theorem - Discrete Random Variable*):

### Discrepancy 1

- The first lecture video, defines the concept of â€śsampleâ€ť, but nowhere in the lecture videos, it has been mentioned that â€śsampleâ€ť could mean 2 different things.
- In fact, the lecture videos themselves use â€śsampleâ€ť for 2 different meanings.
- â€śPopulation and Sample â†’ 0:32â€ť: A sample is a smaller subset that we actually observe or measure (
*Here, it is implying that â€śsampleâ€ť refers to the set of samples*)
- â€śPopulation and Sample â†’ 2:50â€ť: If you thought that the first one was better, that is correct because you always want to take random samples (
*Here, it is implying that â€śsampleâ€ť refers to individual samples*)

- Letâ€™s understand it better with the help of an example. Consider the example of a fair dice being rolled. Now letâ€™s say we roll it 4 times, and get
`(1, 5, 4, 2)`

.
- Here,
`1`

, `5`

, `4`

and `2`

are individually referred to as samples. But the confusing part is that `(1, 5, 4, 2)`

in itself is also referred to as a sample.
- Say, if we roll the dice for 4 more times, and letâ€™s say we get
`(2, 6, 3, 1)`

. Here, this is another sample, consisting of 4 samples.
- Therefore, for our discussion, we have 2 samples, each consisting of 4 samples, i.e., we have a sample-size of 4.
- For reference, check out this video, time-stamp 2:10 onwards. Unlike Luis, Sal has explicitly mentioned this.

### Discrepancy 2

- In the Coursera videos, the same notation has been used for different concepts in different videos.
- Now, off course, we can use the same variable to denote different things, after all, they are variables
- But I believe that the important parameters should always be denoted with different variables, and the notation should be consistent throughout the videos.
- You will find that in the lecture videos â€śnâ€ť denotes both sample-size, as well as the number of samples. I am mentioning 2 references here:
- â€śPopulation and Sample â†’ 2:03â€ť: In this example, the population size is 10,000, denoted by N, and the sample size could be anything smaller, from 1-9,999, thatâ€™s denoted by n (
*Here, n denotes sample size*)
- â€śLaw of Large Numbers â†’ 2:32â€ť: So if n is the number of samples (
*Here, n denotes number of samples*)

## Conclusion

- These are the 2 major discrepancies which I could observe as of now, and a lot of minor discrepancies, and in my opinion, these make the videos more confusing than they make them beneficial for the learners.
- In fact, you are not the first one to feel that the videos are confusing or incorrect. Check out the following threads: Thread 1 and Thread 2.
- At this, point I would suggest you to follow other content on the web, since the team is still working on fixing the content. For starters, you can check out this playlist, which I believe is an amazing source of knowledge.

P.S. - @lucas.coutinho please take a note of this thread. Currently, the content of Week 3, Lecture 1 is extremely confusing, and needs some major changes on an urgent basis. I guess, adding a note regarding this can be helpful for the learners, so that they can avoid getting confused meanwhile.

Cheers,

Elemento

1 Like

Thanks for the reply and the resources @Elemento !

Hi @karthikeyan_S2 and @Elemento!

Thanks for this topic. We are aware of the inconsistency in the CLT videos. We will record again a new version as soon as possible and I have forwarded this thread to our team so they can have a look at it.

Thanks,

Lucas