AP Psychology
Inferential Statistics
Sampling and Selection
Essential Question
• 1-5 How do psychologists draw
appropriate conclusions about behavior
from research?
Inferential Statistics
• You are trying to reach conclusions that
extend beyond the immediate data alone.
• Used to test hypothesis about samples.
Testing for Differences
•If we have results from two samples we can ask
the question:
Is there a difference between
the means of the two the samples?
Testing for Differences
•Using the assumption of a normal distribution, we
can also ask the question:
How confident are we that the mean difference is
genuinely represented in the population rather than
just due to random variation?
•Using inferential statistics, we can ask this
question using a t-test when comparing two groups.
P values and statistical significance
•Generally, the t test gives a P value that allows us a
measure of confidence in the observed difference.
•It allows us to say that the difference is real and
not just by chance.
•The smaller the p value the least chance of making
a false positive (Type I error).
•A p value of less than 0.05 is a common criteria for
significance.
•We call this statistically significant
An Example Experiment
•Does caffeine improve our reaction time?
•We recruit 40 people and give
»20 a cup of coffee
»20 a cup of decaffeinated coffee
•We give them a brief reaction time test and record
the results.
Inferential Statistics
•Caffeine condition
»Mean = 500.32ms
»SD = 172.60ms
•No Caffeine condition
»Mean = 608.64ms
»SD = 146.93
Effect of Caffeine on RT?
Caffeine
No Caffeine
t-test results
•Does caffeine improve our reaction time?
•Caffeine condition has a lower mean RT.
•We run a t-test on our samples and get:
»p = 0.039
•Can we be confident that the difference in the data
is not due to chance? (P Value video)
•ANOVA An ANOVA (Analysis of Variance), sometimes called an F
test, is closely related to the t test. The major difference is that, where the
t test measures the difference between the means of two groups, an
ANOVA tests the difference between the means of two or more groups.
Video notes
• 1) P value refers to the likelihood your research results
were by chance. It usually has to be below .05 for the
differences you found to have any real meaning.
• 2) F critical is the value that must be "crossed" in order
for p value to be significant.
• 3) If F critical is passed, then the p value gets glory (so
to speak) because it is now significant.
• 4) Bonferroni is a conservative stats adjustment used
when making many comparisons between effects. it
makes it more likely that p will be non-significant and that
F critical will not "have her threshold" crossed.
Within / Between Group Comparison
•Two ways of comparing people:
•Within groups (everybody does all conditions)
»e.g. ‘before and after’ study
Condition 2:
Sleep Deprived
Condition 1:
Slept Well
Time
Test memory
Test memory
again
Within / Between Group Comparison
•Between groups (different people do each
condition)
»e.g. ‘males vs females’ study
Group 1:
Males
Group 2:
Females
Test visual
abilities
Test visual
abilities
Summary
•Descriptive statistics give a summary of the data.
•Statistical dispersion describes the spread of the
data.
•Many attributes are normally distributed.
•Inferential statistics allow us to test hypotheses
using this assumption.
•Studies need to be carefully designed to make this
meaningful.
Essential Question
• 1-5 How do psychologists draw
appropriate conclusions about behavior
from research?