Hypothesis Testing and T-Tests in SPSS

What is Hypothesis Testing?

Hypothesis testing is a statistical method to determine whether there is enough evidence in a sample to support a claim about a population.

Key Concepts

• Null Hypothesis (H₀): There is NO significant difference/relationship
• Alternative Hypothesis (H₁): There IS a significant difference/relationship
• Significance Level (α): Usually 0.05 (5%)
• P-value: The probability of getting results as extreme as observed if H₀ is true

Decision Rule

Independent Samples T-Test

When to use: Compare means between two independent groups (e.g., males vs. females).

Example Hypothesis:

"There is a statistically significant difference in job satisfaction between male and female employees."

SPSS Steps:

1. Analyze → Compare Means → Independent-Samples T Test
2. Move the dependent variable (e.g., Satisfaction_Avg) to "Test Variable(s)"
3. Move the grouping variable (e.g., Gender) to "Grouping Variable"
4. Click Define Groups: Group 1 = 1, Group 2 = 2
5. Click OK

Reading the Output:

1. Check Levene's Test first (tests equality of variances):
   • If Sig. > 0.05 → use "Equal variances assumed" row
   • If Sig. ≤ 0.05 → use "Equal variances not assumed" row
2. Look at Sig. (2-tailed) in the appropriate row:
   • If < 0.05 → significant difference → reject H₀
   • If ≥ 0.05 → no significant difference → fail to reject H₀

One-Sample T-Test

When to use: Compare the sample mean against a known/hypothetical value.

Example: "Is the average satisfaction score significantly different from 3 (neutral)?"

SPSS Path: Analyze → Compare Means → One-Sample T Test

Set Test Value = 3

Paired Samples T-Test

When to use: Compare means for the same group at two different times (pre-test vs. post-test).

SPSS Path: Analyze → Compare Means → Paired-Samples T Test

Remember: The T-test only works for comparing two groups. For three or more groups, use ANOVA (next lesson).