Poisson Hypothesis Testing (CIE A Level Maths: Probability & Statistics 2)

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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 straight P left parenthesis X less or equal than c right parenthesis less or equal than alpha percent sign and straight P left parenthesis X less or equal than c plus 1 right parenthesis greater than alpha percent sign then c is the critical value
      • For H1 : λ > ... if straight P left parenthesis X greater or equal than c right parenthesis less or equal than alpha percent sign and straight P left parenthesis X greater or equal than c minus 1 right parenthesis greater than alpha percent sign 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
  • 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 begin mathsize 16px style X tilde Po left parenthesis lambda right parenthesis end style  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

    • Or if told to find the critical region

Step 5.  Compare this probability with the significance level

    • 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.

(i)
State null and alternative hypotheses for Mr Viajo’s test.

 

(ii)
Find the rejection region for the test.

 

(iii)
Find the probability of a Type I error.

 

(iv)
Carry out the hypothesis test, writing your conclusion clearly.

3-2-2-poisson-hyp-testing-we-solution-

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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Amber

Author: Amber

Expertise: Maths

Amber gained a first class degree in Mathematics & Meteorology from the University of Reading before training to become a teacher. She is passionate about teaching, having spent 8 years teaching GCSE and A Level Mathematics both in the UK and internationally. Amber loves creating bright and informative resources to help students reach their potential.