1. AP PSYCHOLOGY
Descriptive and Inferential Statistics
Example from “A Lesson on
Correlation”, AP Psychology
Curriculum Module. Amy Fineburg,
2009.
Adaptations created by R. Lochel &
M. Kunz, 2011 & 2012

2. Case Study- Pellegra
• In the early twentieth century,
thousands of Americans in the
South died from pellagra, a
disease marked by dizziness,
lethargy, running sores, and
vomiting.
• Finding that families struck
with the disease often had poor
plumbing and sewage, many
physicians concluded that
pellagra was transmitted by
poor sanitary conditions.

3. DESIGNING A STUDY- Components of experimentation-
Plan to execute, execute the plan
• Independent variable: Experimenter
manipulated
• Dependent variable: Behaviors
measured/observed
•Is the conclusion plausible?
–Are there alternate explanations?
•Confounding variables
identified.

4. Types of Studies- Methods of Data
Collection
Pro
– Obtain detailed
information about the
characteristics and
behaviors of subjects.
– May discover unusual
features.
Cons
• Cannot generalize to the
population.
• Might be subject to cultural
and societal values (which
could change over time)
Case study: choose a number of subjects to study a
singular behavior in detail. Summarize results.
Attempt to find parallels to other cases.

5. Types of Studies- Methods
Correlational study Organize existing data into
explanatory and response variables:
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
%ofpeoplewhocontractPellegra
% of homes with poor plumbing / sewage
Sanitation vs Pellegra Rates

6. Types of Studies
• Correlational study: examine the strength of
the relationship between variables.
Pros:
Can reveal relationships.
Allows for prediction.
Demonstrates patterns of
behavior
Indicates likelihood that one
behavior may happen in the
presence of another.
Cons:
Cannot be used to prove
cause and effect.
Often misidentified as
causation.

7. Case Study- Pellegra
• In contrast, Public Health Service
doctor Joseph Goldberger
thought that the illness was
caused by an inadequate diet.
• He felt that the correlation
between sewage conditions and
pellagra did not reflect a causal
relationship, but that the
correlation arose because the
economically disadvantaged
were likely to have poor diets as
well as poor plumbing.

8. DESIGNING A STUDY
• Goldberger sought to prove that diet, not
sewage, was the cause of pellegra.
• In order to “prove” a cause-effect
relationship, an experiment must be designed
and carried out.

9. DESIGNING A STUDY- Experimentation
• Experimental design
• CONTROL Group: Need a comparison group in order to reduce
alternate explanations. Base group provides “natural” conditions.
• Subjects, Sampling, & RANDOMIZATION: Subjects must be randomly
assigned to treatments, in order to “smooth out” confounding
variables. Minimizes bias. Randomization helps to generalize results.
• Polls and Politics example of sampling not of experimentation- psych
in action!
Read the article.
• REPLICATION: The experiment should be carried out with enough
subjects (Minimum 50) to establish a clear pattern. Other skeptics
should be able to test your hypothesis (and find the same conclusions)
to ensure reliability and validity.

10. Sampling and Polls- Politics and Psych
Briefing:
• Polling as we know it today began in 1936, when a young statistician
named George Gallup conducted the first poll using statistical modeling.
He accurately predicted that Franklin D. Roosevelt would trounce Alf
Landon. For decades after that, the polling bus.
• How does polling work?
Sampling public opinion, George Gallup once said, is like sampling soup:
One spoonful can reflect the taste of the whole pot, if the soup is well-
stirred.
• In other words, it’s all about finding a sample that reflects the larger
population. Polling is based on the laws of probability.
• According to probability theory, it’s not necessary to sample the opinions
of all 300 million Americans; a much smaller sample can reflect the larger
population—if that sample is truly representative.
• For national polls, most pollsters use a sample of 1,500 as a rule of thumb

11. DESIGNING A STUDY
• Experiment: use of a comparison group to
establish a relationship
• Pros:
– Can be used to establish cause-effect relationships.
– Stress the comparison of variables
• Cons:
– May be difficult to simulate “real-world” conditions.
– Ethical concerns

12. Pellegra Study Design
• Goldberger asked two groups from
a Mississippi state prison farm to
volunteer for an experiment (in exchange
for a pardon from their crimes). Pellegra
didn’t pre-exist in the prison.
• Group 1 (Experimental Group) was given the high-
carbohydrate, low-protein diet (independent variable) that
Goldberger suspected to be the culprit, while Group 2 (Control
Group) received a “normal” balanced prison diet.
• Within months, the former group was ravaged by pellegra
(measured dependent variable), while the latter showed no signs of
the disease.

13. Pellegra Conclusions
• The pellagra symptoms disappeared when the volunteers were given
meat, fresh vegetables, and milk.
• Despite this conclusive evidence, Goldberger had trouble convincing others
what he had found.
• He spent the rest of his life looking for what exactly was missing in the diet
that caused pellagra, but this would not be uncovered until after his death.
He also was thwarted by the medical world's obsession with infectious
disease, newly understood and in some cases treatable, and the political
world's resistance to hearing that poor social conditions could cause
disease.
• In 1937, researcher Conrad Elvehjem found that nicotinic acid, or
niacin, prevented and cured pellagra in dogs. It works as well in humans.
Niacin is one of the B vitamins. During the 1930s, great strides were made
in understanding the way vitamins work in the chemistry of our bodies.

14. DESCRIPTIVE STATISTICS
• What comparisons can we make between the
heights of males and females?

15. DESCRIPTIVE STATISTICS
• Descriptive Statistics describe sample data
• Measures of Center
– Mean
– Median

16. DESCRIPTIVE STATISTICS
• Measure of Spread
– Standard deviation

17. DESCRIPTIVE STATISTICS
• A man has a height of 6’4”. How unusual is his
height, relative to the population of adult men?

18. DESCRIPTIVE STATISTICS
• The Normal Distribution
– An idealized distribution
of a population.
– Heights
– IQ scores
– “Natural” phenomena
– Sampling distributions
• The 68-95 rule

19. DESCRIPTIVE STATISTICS- Psych
Example

20. INFERENTIAL STATISTICS- Apply to
Goldberger Study
• What conclusions can be drawn from the Pellagra
study?
– An effect is present (weak diet causes Pellagra)
– The results can be explained by the random assignment of
subjects to treatments.
• Inferential Statistics allow us to reach conclusions
which extend beyond the immediate data alone.

21. INFERENTIAL STATISTICS- Hypothesis
Matters
• In Statistics, a hypothesis test allows us to measure the
results of an experiment
• Here, the default hypotheses (or the null hypothesis)
states that there is no difference between the two
treatments in the development or Pellagra.
• The alternate hypothesis says that the treatments
make a difference in the onset of Pellagra (cause and
effect).
–Researchers usually seek to
prove the alternate hypothesis.

22. INFERENTIAL STATISTICS- Probability in
Psychology-
•P-value
– The probability of observing the measured results of an
experiment to determine if no effect is present.
– A low p-value (usually below 5% or valued at .05) means
the results are statistically significant.
» A low p-value often allows us to determine that
an effect is present, and is applicable to
generalize conclusions to a population.
» P-Value < .05 = results are due to more than
chance/coincidence. Conclude that there is a
cause-effect relationship.

23. INFERENTIAL STATISTICS- Probability in
Psychology-