Reference no: EM133388700

Question: Run the above code several times (without changing it) and look at the histograms and how they change. Sometimes you will see that the means are pretty good estimates of the true population means (10 and 20) and sometimes they are not. Add a markdown cell below this and answer the following questions:

1) If, in a random draw of a single value, you got a value of -20, which population is it most likely to belong to? Why?

2) What if you drew a value of 20 in a random draw. Would you be able to tell which population the score came from? Why or why not?

# Change this cell to markdown and answer the above questions.
Now, change the population_sd from 20 to 10. Run the code again a few times and see how the histograms change. Answer the following questions in a markdown cell below.

3) Based on what you have learned about Probability Density Functions, is it likely (in a random draw of a single value) that a score of 0 came from the Mean_1 population? Why or Why not?

4) If you drew a random sample of 20 participants from one of these populations and the sample mean was 25, what would you conclude about which population the sample came from? Why?

# Change this cell to markdown and answer the above questions.
Now, change the population_sd to 5. Run the code again a few times and look at the histograms. Answer the final question in the markdown cell below.

5) It is now much easier to tell the two samples apart and to link them to their respective populations. It is now highly likely that a score with a value of 0 is from the Mean_1 populaton and a score with a value of 25 is from the Mean_2 populaton. However, there is still a fair amount of overlap between them. Without changing the means or changing the population_sd, is there anything you could do to enhance your ability to visually differentiate these populations? What is it and why does it help?