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Statistics for the Behavioral Sciences (5 th ed.) Gravetter & Wallnau

Statistics for the Behavioral Sciences (5 th ed.) Gravetter & Wallnau. Chapter 8 Introduction to Hypothesis Testing. University of Guelph Psychology 3320 — Dr. K. Hennig Winter 2003 Term. The logic. experience-> question (What is it? Why…?)-> insight (hypothesis)-> “Is it so?”

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Statistics for the Behavioral Sciences (5 th ed.) Gravetter & Wallnau

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  1. Statistics for the Behavioral Sciences (5th ed.)Gravetter & Wallnau Chapter 8Introduction to Hypothesis Testing University of GuelphPsychology 3320 — Dr. K. HennigWinter 2003 Term

  2. The logic • experience-> question (What is it? Why…?)->insight (hypothesis)->“Is it so?” • As text has it: • State your hypothesis (e.g., MIQ for voters is =110) • thus we would predict that our sample M = 110 • Obtain a random sample from the population (e.g., n = 200 registered voters) and compute M • Compare M with predicted M Intellig-ability Intellig-ence

  3. Fig. 8.2 Population a) actual research situation TreatedSample Sample Tx b) pt. of view of hypothesis test Population TreatedSample Tx

  4. Step 1: State the hypothesis • Question: does handling a infant have an effect on body weight? • null hypothesis stated: assume that in the general population there is no change, no effect, no difference (nothing happened) • H0: infants handled = 26 lbs. (even with handling) • the alternative hypothesis states there is a change, effect, difference • H1: infants handled <> 26 lbs. (handling makes a difference) - both ref. to popultns

  5. Step 2: Set the criteria • If the Ho is true, sample means will be close to the null hypothesis • unlikely sample means will be very different from the null hypothesis (in the tails of the distribution) • criteria separating the likely from the unlikely sample • Alpha level ( or level of significance): p value used to define the unlikely sample • critical regions: very unlikely if the null hypothesis is true - if sample falls within, reject null hypothesis

  6. Set the criteria (contd.) •  = .05 (boundaries separate the extreme 5% from the middle 95%) • see Column C (the tail) in the tail: z = 1.96 and z = -1.96 • Similarly,  = .01, 99%: z =  2.58 • Similiary,  = .001: z =  3/30

  7. Step 3: Collect data • Select parents and randomly assign to training program of daily handling (= Tx) • Weigh after 2 years • summarize the data using the appropriate statistics (e.g., M) • Compare with the null hypothesis by transforming into z-score

  8. Step 4: Make a decision (“It is/not so!”) • Calculate: M = 30 lbs. at age 2; sample size = n = 16, and  = 4

  9. (contd.) • Why do we focus on the null hypothesis? Why assume there is no change? • negative thinking? • “innocent until proven guilty?” - burden of proof • “Is it so?” vs. “Is it not so?” • Logically, easier to falsify vs. verify (?) • E.g., All dogs have four legs! • E.g., state, the Tx works and then try and prove vs. the Tx has no effect and try to show false (conclude: insufficient evidence)

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