Comparing Two Means: Examples & Assumptions

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In summary, the conversation discusses the assumption for a 2-sample t confidence interval and the likelihood of it failing. The example provided is comparing the mean heights of males and females on a campus where females are predominantly Asian, leading to a difference in variances. However, it is uncertain if this is a good example as it assumes a specific variance for Asian females. The individual requests for further help with providing a concrete example.
  • #1
EvLer
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It's sort of a conceptual question:
one of the assumptions for 2-sample t confidence interval is equality of variances in two populations, and I need to come up with a concrete example of when this is likely to fail.
I sort of have an idea that it really depends how one chooses samples. For example, if one compares mean hight of males and females on a certain campus where females are predominantly asian. Then since variance in hight of asian women is much smaller than variance in hight of white males, the assumption fails. ALthough I am not sure if this is a good example because I make an explicit assumption that asian females have small variance in height. Could someone help me out a bit with an example?
Thanks in advance.
 
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  • #2
When what is likely to fail?
 
  • #3
equality of the variances of the 2 populations
 
  • #4
The probability of the two variances being equal is exactly zero.
 

What is the purpose of comparing two means?

The purpose of comparing two means is to determine if there is a significant difference between the means of two groups or samples. This can help researchers understand if there is a relationship between the variables being compared and if the results are reliable and generalizable.

What are the assumptions for comparing two means?

There are several assumptions that must be met in order to accurately compare two means. These include:

  • The data for each group must be normally distributed
  • The variances of the two groups should be equal
  • The observations for each group should be independent of each other
  • The sample size for each group should be sufficiently large (usually n ≥ 30)
If these assumptions are not met, alternative statistical tests may need to be used.

What is an example of comparing two means?

An example of comparing two means would be a study that compares the average test scores of students who received a new teaching method versus those who received the traditional teaching method. The mean test scores of the two groups would be compared to determine if there is a significant difference in the effectiveness of the two teaching methods.

How do you interpret the results of a two means comparison?

The results of a two means comparison are typically presented in the form of a p-value. A p-value less than 0.05 indicates that there is a significant difference between the two means, while a p-value greater than 0.05 suggests that there is not a significant difference. It is important to also consider the effect size and confidence intervals when interpreting the results.

What are some limitations of comparing two means?

One limitation of comparing two means is that it does not account for other variables that may influence the results. Additionally, if the assumptions for comparing two means are not met, the results may not be accurate. It is also important to consider the sample size and potential biases in the data when interpreting the results.

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