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Poisson Hypothesis Testing (CIE A Level Maths: Probability & Statistics 2)
Revision Note
Poisson Hypothesis Testing
How is a hypothesis test carried out for the mean of a Poisson distribution?
- The population parameter being tested will be the mean, λ , in a Poisson distribution
- As it is the population mean, sometimes μ will be used instead
- A hypothesis test is used when the mean is questioned
- The null hypothesis, H0 and alternative hypothesis, H1 will be given in terms of λ (or μ)
- Make sure you clearly define λ before writing the hypotheses
- The null hypothesis will always be H0 : λ = ...
- The alternative hypothesis will depend on if it is a one-tailed or two-tailed test
- A one-tailed test would test to see if the value of λ has either increased or decreased
- The alternative hypothesis, will be H1 will be H1 : λ > ...or H1 : λ < ...
- A two-tailed test would test to see if the value of λ has changed
- The alternative hypothesis, H1 will be H1 : λ ≠ ...
- To carry out a hypothesis test with the Poisson distribution, the random variable will be the mean number of occurrences of the event within the given time/space interval
- Remember you may need to change the mean to fit the interval of time or space for your observed value
- When defining the distribution, remember that the value of λ is being tested, so this should be written as λ in the original definition, followed by the null hypothesis stating the assumed value of λ
- The Poisson distribution will be used to calculate the probability of the random variable taking the observed value or a more extreme value
- The hypothesis test can be carried out by
- either calculating the probability of the random variable taking the observed or a more extreme value and comparing this with the significance level
- or by finding the critical region and seeing whether the observed value of the test statistic lies within it
- Finding the critical region can be more useful for considering more than one observed value or for further testing
How is the critical value found in a hypothesis test with the Poisson distribution?
- The critical value will be the first value to fall within the critical region
- The Poisson distribution is a discrete distribution so the probability of the observed value being within the critical region, given a true null hypothesis may be less than the significance level
- This is the actual significance level and is the probability of incorrectly rejecting the null hypothesis (a Type I error)
- For a one-tailed test use the formula to find the first value for which the probability of that or a more extreme value is less than the given significance level
- Check that the next value would cause this probability to be greater than the significance level
- For H1 : λ < ... if and then c is the critical value
- For H1 : λ > ... if and then c is the critical value
- Using the formula for this can be time consuming so only use this method if you need to
- otherwise compare the probability of the random variable being at least as extreme as the observed value with the significance level
- Check that the next value would cause this probability to be greater than the significance level
- For a two-tailed test you will need to find both critical values, one at each end of the distribution
- Take extra care when finding the critical region in the upper tail, you will have to find the probabilities for less than and subtract from one
What steps should I follow when carrying out a hypothesis test with the Poisson distribution?
Step 1. Define the mean, λ
Step 2. Write the null and alternative hypotheses clearly using the form
H0 : λ = ...
H1 : λ = ...
Step 3. Define the distribution, usually where λ is the mean to be tested
Step 4. Calculate the probability of the random variable being at least as extreme as the observed value
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- Or if told to find the critical region
Step 5. Compare this probability with the significance level
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- Or compare the observed value with the critical region
Step 6. Decide whether there is enough evidence to reject H0 or whether it has to be accepted
Step 7. Write a conclusion in context
Worked example
Mr Viajo believes that his travel blog receives an average of 8 likes per day (24 hour period). He tries a new advertising campaign and carries out a hypothesis test at the 5% level of significance to see if there is an increase in the number of likes he gets. Over a 6-hour period chosen at random Mr Viajo’s travel blog receives 5 likes.
Examiner Tip
- Take extra careful when working in the upper tail in Poisson distribution questions, this is where its easy to make mistakes.
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