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Lecture 3

Lecture 3 - Jan 16.doc

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McGill University
PSYC 305
Heungsun Hwang

PSYC305 Lecture 3 - Jan. 16 Hypothesis: Answer to a research question or assumption made about a population parameter (not a sample estimate!) • Ad campaign A is preferred over campaign B • Getting 1 million will make people happier 6 months later • Drug A will increase survival rate of AIDS patients Steps for Hypothesis Testing: • Step 1: Set up a hypothesis • Usually a prediction that there is an effect of certain variable(s) in the population • Example: Hamburgers make you fat! • Null Hypothesis (Ho): No effect • • People will be equally fat regardless of how many hamburgers you eat • Alternative Hypothesis (H1): • Some effect • People eating more hamburgers will be fatter than those eating less hamburgers Step 2: Choose alpha (significance level) • • Decide the area consisting of extreme scores which are unlikely to occur if the null hypothesis is true • Conventionally, alpha = .05 (or .01) • The cutoff sample score for alpha is called the critical value Step 3: Example empirical data and compute the appropriate test statistics • • Step 4: Make the decision whether to ‘reject’ or ‘not reject’ the null hypothesis • Compare the calculated value of your test statistic to the (tabled) critical value for alpha • If your value is greater than the critical value, reject H 0 • Otherwise, accept H 0 • Alternatively, look at at the significance level (p-value) of your test statistic value • If p-value < .05, reject H 0 • If H0is rejected, you may conclude that there is statistically significant effect in the population • Hamburgers have a significant effect on being fat A “significant” effect does not indicate that: • This effect is important or meaningful: • 10g weight gain by eating hamburgers a month • This weight gain may be significant when it was observed from many people • But, this weight gain is really meaningful in our real l
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