Testing Hypotheses for Binomial Distributions: A Beginner's Guide

In summary, the conversation discusses how to test hypotheses for any distribution, specifically using the example of X and theta in a binomial distribution. The goal is to reject the null hypothesis (H0) at a significance level of 0.05. Techniques such as the binomial and likelihood ratio statistic are mentioned, with the recommendation to use the binomial method for this particular problem. The conversation ends with some confusion regarding the use of the mean in the binomial distribution.
  • #1
LBJking123
13
0
Hi, I am trying to teach myself how to test hypotheses for any distribution, but am having some trouble.

X=number chosen each year
θ=Mean number chosen in the population

H0: θ=.5
h1: θ>.5

The random sample of n=4 is 0,1,3,3

Test the Hypotheses at α≤0.05 assuming X is a binomial(5,θ/5).

I am completely lost with how to even start this problem. Any help would be awesome.
 
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  • #2
Hey LBJking123.

First try calculating the value of H0 by finding the probabilities corresponding to theta = 0.5

A hint for this is to get the estimator distribution for theta. You are assuming that X is binomial (5,theta/5), so you need to get the mean and use that as an estimator for theta.

After this you have to use a test to get your final statistic and this can range from using the binomial distribution directly to using something like a likelihood ratio statistic.

What techniques have you covered in class?
 
  • #3
Thanks chiro!
We have covered both of those methods (binomial and likelihood ratio statistic), but I think we are supposed to use the binomial to do this one.
This is what I have so far (I am not very confident):

Sample average = 1.75

So,

Reject H0 if P(X≥1.75, given that X is binomial(5,.1)) ≤ 0.05

Then I figure out 1-P(X≤1.75)=0.08146 which is greater than 0.05 so I reject the null.

It just seems like I am totally missing something...

The mean on the binomial(5,theta/5) is just theta. I don't understand how that helps though.
 
Last edited:

Related to Testing Hypotheses for Binomial Distributions: A Beginner's Guide

What is the purpose of testing hypotheses binomial?

The purpose of testing hypotheses binomial is to determine the statistical significance of a relationship between two categorical variables. It allows scientists to determine if there is a significant difference between observed and expected frequencies, which can help support or reject a hypothesis.

How is a binomial test different from a chi-square test?

A binomial test is used when there are only two possible outcomes for a categorical variable, while a chi-square test can be used for any number of possible outcomes. Additionally, a binomial test focuses on comparing observed and expected frequencies, while a chi-square test also takes into account the sample size and degrees of freedom.

What is the null hypothesis in a binomial test?

The null hypothesis in a binomial test is that there is no significant difference between observed and expected frequencies. This means that any observed differences are due to chance and not a true relationship between the variables.

What is the alternative hypothesis in a binomial test?

The alternative hypothesis in a binomial test is that there is a significant difference between observed and expected frequencies. This means that any observed differences are not due to chance and there is a true relationship between the variables.

How do you interpret the results of a binomial test?

The results of a binomial test are typically presented as a p-value, which represents the probability of obtaining the observed results by chance. If the p-value is less than the chosen significance level (usually 0.05), then the null hypothesis is rejected and the alternative hypothesis is supported. If the p-value is greater than the significance level, then the null hypothesis cannot be rejected and there is no significant difference between observed and expected frequencies.

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