Pscyh_7_NOTES_11_17_09 - 2 - Class 211ltoti3.1tlr ~Ubtnll...

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Copyright © 1996 ASUCSB Reproduction in any form is a violation of Federal Copyright Laws. .S. Notetaking • 893-4471 • Open 10:00 - 4:00 Monday - Friday • Closed Finals Week Class Date 211ltoti3.1tlr ~Ubtnll ~oteta:hin!J $erbice Notetaker Psychology 7 11/17/09 Revlin . Lec;.Ht' \ S Next Exam 12/09/09 Page 1 of 5 ANNOUNCEMENTS OUTLINE OF LAST LECTURE XVIII Surveys (cont.) A. Types ofsamples B. Worrisome questions OUTLINE OF TODAY'S LECTURE XIX. Analyzing Data A. Inferential Statistics B. Null vs. Research Hypothesis C. t-test and Samples D. Effect Size TODAY'S LECTURE Analyzing Data The basic problem that a research has is decidingwhether observed differences in the sample of participants reflect differences in the population asa whole. To evaluate data to answer this problem requires the use of inferential statistics rather than descriptive statistics. The latter describes the sample, the former assesses how likely the fmdings are if 100 samples or more from the population were used. Most research designs intended to provide evidence that one variable caused another 'The simplest case: does the mean score in one experimental group differ from another group? It often takes the form ofcomparison between a control group and experimental group, but more complicated designs are possible. Inferential statistics This type ofstatistics allows researchers to make inferences about the true difference in the population on the basis ofthe sample data and gives the probability that the difference between means reflects random error rather than a real difference. However, ifthe groups are initially equivalent, then any differences in the dependent variable must be due to the effect ofthe independent variable. . . In general, Inferential statistics allow us to answer the question: Ifwe conduct the experiment 100 times with new samples each time, what's the likelihood we'd get the same results.
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Page 2 of5 Statistical Significance "statistical significance" assesses the probability that results could be due to chance rather than the hypothesized cause. For example, can observed difference (in means) be accounted for just by chance? Where "chance" is everything *not* accounted for in your manipulation Another way to think of statistical significance is a measure ofhow likely it is that we have a true or real difference between groups.
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Pscyh_7_NOTES_11_17_09 - 2 - Class 211ltoti3.1tlr ~Ubtnll...

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