Edexcel International A Level Maths: Statistics 2

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2.2.1 Binomial Hypothesis Testing

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Binomial Hypothesis Testing

How is a hypothesis test carried out with the binomial distribution?

  • The population parameter being tested will be the probability, p  in a binomial distribution B(n , p)
  • A hypothesis test is used when the assumed probability is questioned
  • The null hypothesis, H0  and alternative hypothesis, H1 will always be given in terms of p
    • Make sure you clearly define p before writing the hypotheses
    • The null hypothesis will always be H: p = ...
    • 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 p  has either increased or decreased
      • The alternative hypothesis, H1 will be H1 : p > ...  or  H1 : p < ...  
    • A two-tailed test would test to see if the value of p  has changed
      • The alternative hypothesis, H1 will be H1 : p ≠ ... 
  • To carry out a hypothesis test with the binomial distribution, the test statistic will be the number of successes in a defined number of trials
  • When defining the test statistic, remember that the value of p  is being tested, so this should be written as p in the original definition, followed by the null hypothesis stating the assumed value of p
  • The binomial distribution will be used to calculate the probability of the test statistic taking the observed value or a more extreme value
  • The hypothesis test can be carried out by
    • either calculating the probability of the test statistic taking the observed or a more extreme value and comparing this with the significance level
      • You may have to use a normal approximation to calculate the probability
    • 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 binomial distribution?

  • The critical value will be the first value to fall within the critical region
    • The binomial 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
  • For a one-tailed test use your calculator 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 : p < ... 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 space greater than alpha percent sign then c is the critical value
      • For H1 : p > ...  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
  • For a two-tailed test you will need to find both critical values, one at each end of the distribution
    • Find the first value for which the probability of that or a more extreme value is less than half of the given significance level in both the upper and lower tails
      • Often one of the critical regions will be bigger than the other

What steps should I follow when carrying out a hypothesis test with the binomial distribution?

  • Step 1. Define the probability, p
  • Step 2. Write the null and alternative hypotheses clearly using the form

H0 : p = ...

H1 : p = ...

  • Step 3. Define the test statistic, usually begin mathsize 16px style X tilde B left parenthesis n comma p right parenthesis end style where n  is a defined number of trials and p is the population parameter to be tested
  • Step 4. Calculate either the critical value(s) or the necessary probability for the test
  • Step 5. Compare the observed value of the test statistic with the critical value(s) or the probability with the significance level
  • 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

Jacques, a breadmaker, claims that fewer than 40% of people that shop in a particular supermarket buy his brand of bread.  Jacques takes a random sample of 12 customers that have purchased bread and asks them which brand of bread they have purchased.  He records that 2 of them had purchased his brand of bread.  Test, at the 10% level of significance, whether Jacques’ claim is justified.

2-2-1-binomial-hypothesis-testing-we-solution-part-1

2-2-1-binomial-hypothesis-testing-we-solution-part-2

Exam Tip

  • Most of the time the values of n and p will be in the tables
  • if not, you will have to use the formula to calculate the probabilities or use a normal approximation

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Dan

Author: Dan

Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.