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Lecture 3

# PSYC 305 Lecture Notes - Lecture 3: Standard Deviation, Simple Random Sample, Standard Score

by OC13018

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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 H0

•Otherwise, accept H0

•Alternatively, look at at the significance level (p-value) of your test statistic value

•If p-value < .05, reject H0

•If H0 is 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

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