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Lecture 3

# Lecture 3 - Jan 16.doc

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McGill University

Psychology

PSYC 305

Heungsun Hwang

Winter

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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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