1. Experimental Methods: Statistics & Correlation
Mr. Koch
AP Psychology
Andover High School

2. Statistics Types: Distributions: Descriptive – describe data
Inferential – mathematical procedures that help psychologists make inferences about what data mean
Distributions:
Frequency distribution
Histogram
Percentile rank

3. Central Tendencies Mean (M) – arithmetic average
( ) = 56/16 = 3.5
Median – halfway point in a set of data
( ) = middle #(s)
Mode – score that occurs most frequently
5 = most frequent #

4. Variation Range = gap between highest and lowest scores (5-1)
Standard Deviation (SD)
Calculate each difference (deviation) between each score and the mean
Square these deviations
Find their average
Find the square root of this average
Use a “normal curve” to interpret results

5. Deviation from the Mean (40)
Standard deviation gauges whether scores are packed together or dispersed because it uses information from each score. (The true basis for curving a test.)
M = 160/4 = 40 Sum of (deviations)² = 46
SD = √{[sum of (deviations)²]/number of scores} = √(46/4) = 3.4
Test Scores
Deviation from the Mean (40)
Deviation Squared 36 -4 16 38 -2 4 41 +1 1 45 +5 25

6. The Normal (aka Bell) Curve

7. Negatively skewed Bell Curve Positively skewed Bell Curve

8. Inferential Statistics
Statistical significance
Calculation of the likelihood a result happens by chance
Most use 5% (arbitrary) as standard for determining significant difference between means
p < .05
Principles:
Representative samples are better than biased samples
More cases are better than fewer
Less variable observations are more reliable than highly variable observation

9. Correlation Scatter plot Correlation coefficient
r = One set of scores goes up in direct proportion to the other
r = Scores unrelated
r = One set of scores goes up as the other goes down

10. Examples + (positive) - (negative)
Child abuse / aggressiveness
Education / income
Studying / grades
- (negative)
Self esteem / depression
Age / hours of sleep
Stress / health
Correlation DOES NOT prove causation
Only proves relationship, not cause and effect

11. Correlation “Illusory Correlations”:
A perceived correlation that really does not exist
Examples:
“lucky” penny = wishes come true
Arthritis and cold weather
Pregnant cravings and sex of baby