An analyst for the postal service wants to compare the number of customer visits at two post offices. The analyst counts the number of customers that enter each office for 40 business days.
The analyst performs a 2-sample Poisson rate test to determine whether the daily rate of customer visits differs between the two post offices.
The null hypothesis states that the difference in the daily rate of customer visits between the two post offices is 0. Because the p-value of 0.031 is less than the significance level (denoted as α or alpha) of 0.05, the analyst rejects the null hypothesis and concludes that the daily rate of customer visits differs between the two post offices. The 95% CI indicates that Branch B is likely to have a higher rate of customer visits than Branch A.
|λ₁: Poisson rate of Branch A|
|λ₂: Poisson rate of Branch B|
|Difference: λ₁ - λ₂|
|95% CI for Difference|
|Null hypothesis||H₀: λ₁ - λ₂ = 0|
|Alternative hypothesis||H₁: λ₁ - λ₂ ≠ 0|