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**Sociology 5811: T-Tests for Difference in Means**

Wes Longhofer, pinch-hitting for Evan Schofer

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**Strategy for Mean Difference**

We never know true population means So, we never know true value of difference in means So, we don’t know if groups really differ If we can figure out the sampling distribution of the difference in means… We can guess the range in which it typically falls If it is improbable for the sampling distribution to overlap with zero, then the population means probably differ An extension of the Central Limit Theorem provides information necessary to do calculations!

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**Sampling Distribution for Difference in Means**

The mean (Y-bar) is a variable that changes depending on the particular sample we took Similarly, the differences in means for two groups varies, depending on which two samples we chose The distribution of all possible estimates of the difference in means is a sampling distribution! The “sampling distribution of differences in means” It reflects the full range of possible estimates of the difference in means.

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**Mean Differences for Small Samples**

Sample Size: rule of thumb Total N (of both groups) > 100 can safely be treated as “large” in most cases Total N (of both groups) < 100 is possibly problematic Total N (of both groups) < 60 is considered “small” in most cases If N is small, the sampling distribution of mean difference cannot be assumed to be normal Again, we turn to the T-distribution.

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**Mean Differences for Small Samples**

To use T-tests for small samples, the following criteria must be met: 1. Both samples are randomly drawn from normally distributed populations 2. Both samples have roughly the same variance (and thus same standard deviation) To the extent that these assumptions are violated, the T-test will become less accurate Check histogram to verify! But, in practice, T-tests are fairly robust.

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**Mean Differences for Small Samples**

For small samples, the estimator of the Standard Error is derived from the variance of both groups (i.e. it is “pooled”) Formulas:

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**Probabilities for Mean Difference**

A T-value may be calculated: Where (N1 + N2 – 2) refers to the number of degrees of freedom Recall, t is a “family” of distributions Look up t-dist for “N1 + N2 -2” degrees of freedom.

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**T-test for Mean Difference**

Back to the example: 20 boys & 20 girls Boys: Y-bar = 72.75, s = 8.80 Girls: Y-bar = 78.20, s = 9.55 Let’s do a hypothesis test to see if the means differ: Use a-level of .05 H0: Means are the same (mboys = mgirls) H1: Means differ (mboys ≠ mgirls).

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**T-test for Mean Difference**

Calculate t-value:

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**T-Test for Mean Difference**

We need to calculate the Standard Error of the difference in means:

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**T-Test for Mean Difference**

We also need to calculate the Standard Error of the difference in means:

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**T-test for Mean Difference**

Plugging in Values:

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**T-test for Mean Difference**

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**T-Test for Mean Difference**

Question: What is the critical value for a=.05, two-tailed T-test, 38 degrees of freedom (df)? Answer: Critical Value = approx. 2.03 Observed T-value = 1.88 Can we reject the null hypothesis (H0)? Answer: No! Not quite! We reject when t > critical value

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**T-Test for Mean Difference**

The two-tailed test hypotheses were: Question: What hypotheses would we use for the one-tailed test?

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**T-Test for Mean Difference**

Question: What is the critical value for a=.05, one-tailed T-test, 38 degrees of freedom (df)? Answer: Around (40 df) One-tailed test: T =1.88 > 1.684 We can reject the null hypothesis!!! Moral of the story: If you have strong directional suspicions ahead of time, use a one-tailed test. It increases your chances of rejecting H0. But, it wouldn’t have made a difference at a=.01

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Another Example Question: Do the mean batting averages for American League and National League teams differ? Use a random sample of teams over time American League: Y-bar = .2677, s = .0068, N=14 National League: Y-bar = .2615, s = .0063, N=16 Let’s do a hypothesis test to see if the means differ: Use a-level of .05 H0: Means are the same (mAmerican = mNational) H1: Means differ (mAmerican ≠ mNational)

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**T-test for Mean Difference**

Calculate t-value:

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**T-Test for Mean Difference**

We need to calculate the Standard Error of the difference in means:

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**T-Test for Mean Difference**

We also need to calculate the Standard Error of the difference in means:

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**T-test for Mean Difference**

Plugging in Values:

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**T-test for Mean Difference**

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**T-Test for Mean Difference**

Question: What is the critical value for a=.05, two-tailed T-test, 28 degrees of freedom (df)? Answer: Critical Value = approx. 2.05 Observed T-value = 2.58 Can we reject the null hypothesis (H0)? Answer: Yes We reject when t > critical value What if we used an a-level of .01? Critical value=2.76

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**T-Test for Mean Difference**

Question: What if you wanted to compare 3 or more groups, instead of just two? Example: Test scores for students in different educational tracks: honors, regular, remedial Can you use T-tests for 3+ groups? Answer: Sort of… You can do a T-test for every combination of groups e.g., honors & reg, honors & remedial, reg & remedial But, the possibility of a Type I error proliferates… 5% for each test With 5 groups, chance of error reaches 50% Solution: ANOVA.

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