Why my random experiment has a log normal distribution?

Click For Summary
The simulation results show a log-normal distribution instead of the expected normal distribution due to the nature of the random variable involved. The random variable represents the number of failures before achieving a specific success (the order "EEDCBA"), which aligns with a geometric distribution. The probability of success for this specific order is calculated as 1/360, indicating that the average number of iterations to achieve this order should be 360. The confusion arises from the small sample size of 100, which can superficially resemble a log-normal distribution. Understanding the geometric distribution is essential for interpreting the results correctly.
musicgold
Messages
303
Reaction score
19
Hi,

I am confused with the results of a seemingly simple simulation that is generating a log normally distributed output. Please see the attached results file.

Simulation: I have built a Scratch program that randomly picks six letters from a group of six letters (A, B, C, D, E & E). The program displays the order in which the letters have been picked. I am interested in finding out how many iterations the program takes to get a specific order of letters (say, EEDCBA).

I repeated this experiment 100 times and I was surprised to see log normally distributed results. I was hoping to see a normal distribution with an average of 360.

Can someone please explain what is going on?

Thanks,
 

Attachments

Physics news on Phys.org
The actual distribution should be geometric with p=1/6^6 (i.e. counting the number of failures before a success).

I guess a smallish sample (size 100) would superficially resemble lognormal.
 
I assume you understand, that you cannot actually get neither lognormal nor normal distribution, as they are continuous, and your r.v. is discrete.

If I understood your description, then your r.v. is just "the number of failures, before first success", where success is getting "EEDCBA" and trials are independent, right? In this case what you should get is the geometric distribution.

P.S. I can't open your excel file, so can't give you details of what it's doing wrong
 
Thanks folks.

For those who are not able to open my excel file, I have attached a text file with my results.

If I understood your description, then your r.v. is just "the number of failures, before first success", where success is getting "EEDCBA" and trials are independent, right?
That is correct.

Also, I got the 360 as follows: Prob of getting E in the first place = 2/6, prob of getting the second E in the second place = 1/5, prob of getting D in the third place = 1/4...and so on.


probability of getting EEDCBA = 2/6 * 1/5* 1/4* 1/3 * 1/2 * 1 = 1/360
How is this number related to the distribution? Is it the mean of the distribution?

Also, can you please point to me a source where I can read more about this? I am not sure why I should get a geometric distribution.
 

Attachments

Last edited:
musicgold said:
Thanks folks.
How is this number related to the distribution? Is it the mean of the distribution?

Also, can you please point to me a source where I can read more about this? I am not sure why I should get a geometric distribution.

p = 1/360 is the probability of success. Look at wikipedia article on geometric distribution: it says "geometric distribution [...] is the probability distribution of the number X of Bernoulli trials needed to get one success". Bernoulli trial means a trial which can have only two outcomes: 1 or 0 (or true/false, success/failure etc).

Also, if p is the probability of success, then 1-p is probability of failure. In order to get (first) success on k-th trial (iteration), you need to fail k-1 times in a row and then have a success, thus P(X=k) = (1-p)^{k-1}p, which is exactly the pmf of geometric distribution.

Also, geometrically distrubuted r.v. with parameter p has mean 1/p. So the average number of iterations should indeed be 360.
 
Last edited:
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 12 ·
Replies
12
Views
3K
  • · Replies 13 ·
Replies
13
Views
2K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 2 ·
Replies
2
Views
4K
  • · Replies 3 ·
Replies
3
Views
4K
  • · Replies 61 ·
3
Replies
61
Views
4K
  • · Replies 9 ·
Replies
9
Views
5K
Replies
2
Views
2K