Hypothesis testing

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Hypothesis testing

  1. 1. Hypothesis Testing ELESTA1
  2. 2. Hypothesis Testing <ul><li>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). </li></ul>
  3. 3. An example of data for Hypothesis Testing <ul><li>A researcher wanted to determine the relationship between a students performance in general psychology and his attitude towards the subject. </li></ul><ul><li>Performance was measured through a series of tests in GENPSYC </li></ul><ul><li>Attitude is measured through by the Shore and Shore’s Attitude Scale. </li></ul>
  4. 4. Steps in Hypothesis Testing <ul><li>STEP 1: State the Null and alternative Hypothesis </li></ul><ul><li>H0=There is no significant relationship between attitude and performance. </li></ul><ul><ul><li>r=0 </li></ul></ul><ul><li>H1=There is a significant relationship between attitude and performance </li></ul><ul><ul><li>r=0 </li></ul></ul>
  5. 5. Steps in Hypothesis Testing <ul><li>STEP2: Determine the alpha level of significance, degrees of freedom and critical value </li></ul><ul><li>Alpha level: α=.05, .01 </li></ul><ul><li>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. </li></ul><ul><li>5% or 1% = region of rejection </li></ul><ul><li>95 or 99%=region of acceptance </li></ul>
  6. 6. Steps in Hypothesis Testing <ul><li>Degrees of Freedom (df) </li></ul><ul><li>refers to power of a statistical test </li></ul><ul><li>The more cases the higher the df, then the more probability the sample will represent the population. </li></ul><ul><li>df=n-2 </li></ul>
  7. 7. Steps in Hypothesis Testing <ul><li>Critical value </li></ul><ul><li>Cut-off sample score </li></ul><ul><li>How extreme a sample score is needed to draw a confident conclusion </li></ul>
  8. 8. Steps in Hypothesis Testing <ul><li>STEP 3: Computation </li></ul><ul><li>Formulas are used to determine the obtained or computed value </li></ul>
  9. 9. Steps in Hypothesis Testing <ul><li>STEP 4: Decision Rule </li></ul><ul><li>Decide whether to reject or retain the null hypothesis </li></ul><ul><li>Reject the null hypothesis if the probability of getting a result is less than 5%, p<.05 </li></ul><ul><li>When a sample score is so extreme that researchers reject the null hypothesis, the result is said to be statistically significant </li></ul>
  10. 10. Steps in Hypothesis Testing <ul><li>p < .05/.01 = reject the H0, significant </li></ul><ul><li>p > .05/.01 = retain the H0, not significant </li></ul><ul><li>Obtained value > critical value = reject the H0, significant </li></ul><ul><li>Obtained value < critical value =retain the H0, not significant </li></ul>
  11. 11. Example <ul><li>Ho: There is no significant relationship between attitude and performance </li></ul><ul><li>H1; There is a significant relationship between attitude and performance </li></ul><ul><li>N = 157, α=.05, df=155, r critical=.161 </li></ul><ul><li>r computed = .11, p value=.19 </li></ul><ul><li>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 </li></ul>
  12. 12. Illustration Z=2.03, r=.161 Z=1.38r=.11 2.5% region of rejection 95% 2.5% region of rejection
  13. 13. Decision Errors <ul><li>Type 1 error = if you reject the null hypothesis when in fact the null hypothesis is true </li></ul><ul><li>Type 2 = in reality the research hypothesis is true, but the result doesn’t come out extreme enough to reject the null hypothesis </li></ul>
  14. 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

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