Testing if probability is the same for two groups

In summary, the conversation discusses the need to test if the probability of ##Y_{i}=1## is the same for two different groups at different ages in a logistic regression context. The complication arises from nonlinearity and the question is posed whether testing if the odds of ##Y_{i}=1## is the same for both groups can be used as a default. However, it is unclear what specific test is being referred to.
  • #1
FallenApple
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Ok, so in a logistic regression context, I need to test if the probability of ##Y_{i}=1 ## is the same for two different groups at different ages where age is a continuous variable.

This is actually complicated because of nonlinearity. Can I default to testing if the odds of ##Y_{i}=1 ## is the same for both groups?

Or there is no one to one correspondence between the probability and odds?
 
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  • #2
FallenApple said:
OK, so in a logistic regression context, I need to test if the probability of ##Y_{i}=1 ## is the same for two different groups at different ages where age is a continuous variable.
Can you state more clearly exactly what test you want to do? It is not clear from this description.
 
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Related to Testing if probability is the same for two groups

1. How do you determine if the probability is the same for two groups?

To determine if the probability is the same for two groups, you can perform a statistical test such as a t-test or ANOVA. These tests compare the means of the two groups and determine if there is a significant difference between them. If there is no significant difference, it can be concluded that the probability is the same for both groups.

2. What is the null hypothesis in testing for the same probability in two groups?

The null hypothesis in this case is that there is no significant difference in probability between the two groups. This means that any observed differences are due to chance and not a true difference between the two groups.

3. What is the significance level in testing for the same probability in two groups?

The significance level, also known as alpha, is the threshold at which we reject the null hypothesis. Typically, a significance level of 0.05 is used, meaning that there is a 5% chance of rejecting the null hypothesis when it is actually true. This helps to ensure that our results are not due to random chance.

4. What are the assumptions for testing the same probability in two groups?

The assumptions for testing the same probability in two groups depend on the specific statistical test being used. However, some common assumptions include:

  1. The data is normally distributed
  2. The variances of the two groups are equal
  3. The two groups are independent of each other
If these assumptions are not met, alternative tests or methods may need to be used.

5. What other factors should be considered when testing for the same probability in two groups?

In addition to the statistical tests and assumptions, it is important to consider the sample size, the types of data being compared, and any potential confounding variables. It is also important to carefully interpret the results and consider the practical significance of any differences found between the two groups.

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