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# Hypothesis testing

## by Carlo Magno, Faculty at De La Salle University, Manila on Jan 18, 2011

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## Hypothesis testingPresentation Transcript

• Hypothesis Testing ELESTA1
• 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).
• 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.
• 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
• 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
• 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
• Steps in Hypothesis Testing
• Critical value
• Cut-off sample score
• How extreme a sample score is needed to draw a confident conclusion
• Steps in Hypothesis Testing
• STEP 3: Computation
• Formulas are used to determine the obtained or computed value
• 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
• 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
• 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
• Illustration Z=2.03, r=.161 Z=1.38r=.11 2.5% region of rejection 95% 2.5% region of rejection
• 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
• 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