Chapter 14 Conducting & Reading Research Baumgartner et al Chapter 14 Inferential Data Analysis

Chapter 14 Conducting & Reading Research Baumgartner et al Question: Are the differences observed at the end of an experiment big enough to represent a real difference, taking into account the possibility of sampling error? Assumption: All groups begin an experiment with the same mean when they may not. Sampling error –Occurs when sample not 100% representative of popn –Can be cause of differences between groups “due to chance” –Defined as difference between value of a population parameter and that of the corresponding sample statistic

Chapter 14 Conducting & Reading Research Baumgartner et al Hypothesis Testing 1.State the hypotheses 2.Select the probability level 3.Consult the statistical table 4.Conduct the statistical test 5.Accept or reject the null hypothesis

Chapter 14 Conducting & Reading Research Baumgartner et al Step 1: State the hypotheses null hypothesis (H 0 ): research hypothesis:

Chapter 14 Conducting & Reading Research Baumgartner et al Step 2: Select the probability level Select a probability level at which the null hypothesis will be rejected alpha level (a); Relates the the results of statistical test (step 4)

Chapter 14 Conducting & Reading Research Baumgartner et al Step 3: Consult the statistical table Use appropriate statistical table to determine the value that the statistical test of the sample needs to equal in order to reject the null hypothesis for the selected alpha Often provided as a

Chapter 14 Conducting & Reading Research Baumgartner et al Step 4: Conduct the statistical test Often use a computer, more to come

Chapter 14 Conducting & Reading Research Baumgartner et al Step 5: Accept or Reject Null Hypothesis If p value < alpha value, Otherwise, Significant: Nonsignificant:

Chapter 14 Conducting & Reading Research Baumgartner et al T tests: Used to examine mean scores One group t test –Test whether mean score of a group is equal to hypothesized value Two independent groups t test –Test whether there is a difference in mean scores between two independent groups Two dependent groups t test –Two columns of scores, some degree of correlation or dependency between them repeated measures matched pairs

Chapter 14 Conducting & Reading Research Baumgartner et al Example: One group t test 16 mice given access to two bottles, one with water and one with alcohol Measure their alcohol intake (g/kg) over 13 days

Chapter 14 Conducting & Reading Research Baumgartner et al Calculate t test value t = mean - µ = – 17.0 = 4.03 SE 1.78 degrees of freedom = n-1 = 15 p value = tailed test.