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Lecture 3

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

Course Code
PSYC 305
Heungsun Hwang

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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
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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