Hypothesis testing
Hypothesis testing is a systematic procedure used to determine if sample data supports a theory about a population. It involves stating the null and alternative hypotheses, determining significance levels, computing test statistics, and making a decision to reject or retain the null hypothesis based on the results. An example tested the relationship between student performance on psychology tests and their attitude towards psychology. Hypothesis testing steps were followed: the null hypothesis stated no relationship, the alternative stated a relationship; significance was set at 5%; degrees of freedom and critical values were calculated; the computed test statistic was less than critical so the null hypothesis of no relationship was retained. Hypothesis testing helps researchers make decisions about data while accounting for probability of decision
Hypothesis testing
- 2. Hypothesis Testing A systematic procedure for deciding whether the results of a research study, which examines a sample, support a particular theory or practical innovation, which applies to the population (Aron & Aron (2004).
- 3. An example of data for Hypothesis Testing A researcher wanted to determine the relationship between a students performance in general psychology and his attitude towards the subject. Performance was measured through a series of tests in GENPSYC Attitude is measured through by the Shore and Shore’s Attitude Scale.
- 4. Steps in Hypothesis Testing STEP 1: State the Null and alternative Hypothesis H0=There is no significant relationship between attitude and performance. r=0 H1=There is a significant relationship between attitude and performance r=0
- 5. Steps in Hypothesis Testing STEP2: Determine the alpha level of significance, degrees of freedom and critical value Alpha level: α=.05, .01 5% or 1% of the comparison distribution in which a sample would be considered an extreme that the possibility that it came from a distribution like this would be rejected. 5% or 1% = region of rejection 95 or 99%=region of acceptance
- 6. Steps in Hypothesis Testing Degrees of Freedom (df) refers to power of a statistical test The more cases the higher the df, then the more probability the sample will represent the population. df=n-2
- 7. Steps in Hypothesis Testing Critical value Cut-off sample score How extreme a sample score is needed to draw a confident conclusion
- 8. Steps in Hypothesis Testing STEP 3: Computation Formulas are used to determine the obtained or computed value
- 9. Steps in Hypothesis Testing STEP 4: Decision Rule Decide whether to reject or retain the null hypothesis Reject the null hypothesis if the probability of getting a result is less than 5%, p<.05 When a sample score is so extreme that researchers reject the null hypothesis, the result is said to be statistically significant
- 10. Steps in Hypothesis Testing p < .05/.01 = reject the H0, significant p > .05/.01 = retain the H0, not significant Obtained value > critical value = reject the H0, significant Obtained value < critical value =retain the H0, not significant
- 11. Example Ho: There is no significant relationship between attitude and performance H1; There is a significant relationship between attitude and performance N = 157, α=.05, df=155, r critical=.161 r computed = .11, p value=.19 Decision=since the r obtained which is .11 is less the r critical (.161), the null hypothesis is not rejected. There is no significant relationship between attitude and performance in general psychology
- 12. Illustration Z=2.03, r=.161 Z=1.38r=.11 2.5% region of rejection 95% 2.5% region of rejection
- 13. Decision Errors Type 1 error = if you reject the null hypothesis when in fact the null hypothesis is true Type 2 = in reality the research hypothesis is true, but the result doesn’t come out extreme enough to reject the null hypothesis
- 14. Decision error Type II error β Study inconclusive Do not reject H0 Type I error α H1 is supported Reject Ho Real situation H1 is true Real situation H0 is true