Statistics for the Behavioral Sciences (5th 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?” 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

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

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

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

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

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

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

(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)