Statistics Tutorial: Difference Between Means

Many statistical applications involve comparisons between two independent sample means.

Difference Between Means: Theory

Suppose we have two populations with means equal to μ₁ and μ₂. Suppose further that we take all possible samples of size n₁ and n₂. And finally, suppose that the following assumptions are valid.

• The size of each population is large relative to the sample drawn from the population. That is, N₁ is large relative to n₁, and N₂ is large relative to n₂. (In this context, populations are considered to be large if they are at least 10 times bigger than their sample.)
• The samples are independent; that is, observations in population 1 are not affected by observations in population 2, and vice versa.
• The set of differences between sample means are normally distributed. This will be true if each population is normal or if the sample sizes are large. (Based on the central limit theorem, sample sizes of 40 are large enough).

Given these assumptions, we know the following.

• The expected value of the difference between all possible sample means is equal to the difference between population means. Thus, E(x₁ - x₂) = μ_d = μ₁ - μ₂.
• The standard deviation of the difference between sample means (σ_d) is approximately equal to:
  
  σ_d = sqrt( σ₁² / n₁ + σ₂² / n₂ )

It is straightforward to derive the last bullet point, based on material covered in previous lessons. The derivation starts with a recognition that the variance of the difference between independent random variables is equal to the sum of the individual variances. Thus,

σ²_d = σ² _{(x1 - x2)} = σ² _x1 + σ² _x2

If the populations N₁ and N₂ are both large relative to n₁ and n₂, respectively, then

σ² _x1 = σ²₁ / n₁       And       σ² _x2 = σ²₂ / n₂

Therefore,

σ_d² = σ₁² / n₁ + σ₂² / n₂       And       σ_d = sqrt( σ₁² / n₁ + σ₂² / n₂ )

Difference Between Means: Sample Problem

In this section, we work through a sample problem to show how to apply the theory presented above. The approach presented is valid whenever we need to analyze differences between independent sample means. In this example, differences between means are modeled with a normal distribution; so we use Stat Trek's Normal Distribution Calculator to compute probabilities. The Calculator is free.

Problem 1

For boys, the average number of absences in the first grade is 15 with a standard deviation of 7; for girls, the average number of absences is 10 with a standard deviation of 6.

In a nationwide survey, suppose 100 boys and 50 girls are sampled. What is the probability that the male sample will have at most three more days of absences than the female sample?

(A) 0.025
(B) 0.035
(C) 0.045
(D) 0.055
(E) None of the above

Solution

The correct answer is B. The solution involves four steps.

• Find the mean difference (male absences minus female absences) in the population.
  μ_d = μ₁ - μ₂ = 15 - 10 = 5
  
  
• Find the standard deviation of the difference.
  σ_d = sqrt( σ₁² / n₁ + σ₂² / n₂ )
  σ_d = sqrt(7²/100 + 6²/50) = sqrt(49/100 + 36/50) = sqrt(0.49 + .72) = sqrt(1.21) = 1.1
  
  
• Find the z-score that is produced when boys have three more days of absences than girls. When boys have three more days of absences, the number of male absences minus female absences is three. And the associated z-score is
  z = (x - μ)/σ = (3 - 5)/1.1 = -2/1.1 = -1.818
  
  
• Find the probability. This problem requires us to find the probability that the average number of absences in the boy sample minus the average number of absences in the girl sample is less than 3. To find this probability, we enter the z-score (-1.818) into Stat Trek's Normal Distribution Calculator. We find that the probability of a z-score being -1.818 or less is about 0.035.

Therefore, the probability that the difference between samples will be no more than 3 days is 0.035.