Skip to content
Related Articles

Related Articles

Type II Error in Lower Tail Test of Population Mean with Known Variance in R

View Discussion
Improve Article
Save Article
  • Last Updated : 22 Jun, 2022

Conventionally, In a lower-tail test, the null hypothesis states that the true population mean (μo) is greater than the hypothesized mean value (μ). We fail to reject the null hypothesis if the test statistic is greater than the critical value at the chosen significance level. In this article let us discuss the probability percentage of type II error for a lower tail test of the population mean with known variance.

Type II error is an error that occurs if the hypothesis test based on a random sample fails to reject the null hypothesis even when the true population mean μo is lesser than the hypothesized mean value μ.

Here the assumption is the population variance σ2  is known. From Central Limit Theorem (CLT), the population. the sample means of all possible samples of a population approximately follow a normal distribution. Based on this fact, we can compute the range of sample means for which the type II error can occur, and obtain an estimate of the probability of type II error.

Let us try to understand the type II error by considering a case study. 

Suppose the manufacturer claims that the mean lifetime of a tyre is more than 10,000 km. Assume the actual mean tyre lifetime is 9,950 km and the population standard deviation is 120 km. At the .05 significance level, what is the probability of having a type II error for a sample size of 30 tyres?

Example:

Let us start by computing the standard error of the mean as shown

R




# sample size
no_of_samples = 30
  
# population standard deviation
pop_standard_dev = 120
  
# standard error 
std_error = pop_standard_dev/sqrt(no_of_samples); 
std_error   


Output:

21.9089023002

Then compute the lower bound of sample means for which the null hypothesis μo >= 10000 would not be rejected.

R




# significance level
alpha = .05
  
# sample mean
m0 = 10000
  
# upper bound 
upper_bound = qnorm(alpha, mean=m0,
                    sd=std_error); 
upper_bound


Output:

9963.96

The upper bound value denotes, that as long as the sample mean is greater than 9964 in a hypothesis test, the null hypothesis will not be rejected. Now, compute the probability of the sample mean being greater than 9950 because we have chosen the population mean as 9950. This will later help us to compute the probability of type II error.

R




population_mean = 9950            
pnorm(upper_bound, mean=population_mean,
      sd=std_error,
      lower.tail=FALSE)


Output:

0.2619

If the tyre sample size is 30, the actual mean tyre lifetime is 9,950 hours, and the population standard deviation is 120 km, then the probability of type II error for testing the null hypothesis μ ≥ 10000km at a .05 significance level is 26.2%, and the power of the hypothesis test is 73.8%.


My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!