1. Goals of Statistics
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2. How are you today? A - excited to learn some statistics
B - willing to learn some statistics
C - wish they didn't make us take statistics
D - hung over
E - present
F - up all night finishing grant proposal

3. Overview Types of scientific studies
Independent and dependent variables
Populations and samples
Parameters vs. statistics
Descriptive vs. inferential statistics

4. Scientific Studies Experiment Non-experiment
Involves some sort of intervention
Researcher sets some variable(s) and assesses effect on other variable(s)
Vary number of items in a memory list
Allows inference of causation
Non-experiment
Purely observational
Measure naturally occurring variables and examine relationships
Row of classroom, exam grade
Can't be sure about causality

5. Non-experiments Measure variables without influencing Correlation
Row of classroom, exam grade
Time spent outside, depression
Bicycles currently owned, lifetime head injuries (6, 4)
Apples per week, colds per year
Correlation
Reverse causation
High scores reduce pressure
Motivation
Third variable problem
Health-consciousness
Selection
Math GREs by major
Quasi-independent variables
Height by sex
Row
Score
Attitude
Colds
Apples

6. Experiments X Independent variable Dependent variable
Manipulated by researcher
Drug/placebo, training time, priming
Importance of control
Dependent variable
Measured by researcher
Recovery rate, proficiency, reaction time
Intervention assures causality
Random assignment
Values of IV chosen at random for each subject
Only way to assure causal relationship
3rd variable again
Outright cheating
Time of semester
Attitude
Colds
Apples X
Ability
Performance
Condition

7. Populations and Samples
Set of subjects, items, or events we want to learn about
Generally very large or infinite
All people, all men/women, all pigeons, concrete & abstract nouns, experimental trials (repetitions)
Sample
Subset of population assessed in a given study
Much smaller
Randomly selected
Noisy, not perfectly representative of population (sampling error)
Importance of random selection
Every member of population must have equal chance of inclusion
Otherwise sample is biased
Selection variables may interact with outcome
Undergrads
Racial distribution

8. Parameters Characteristics of the population
Usually theoretically meaningful
Mean, variance, proportion, rate, correlation
What's the average IQ of college students?
How many attempts does it take a normal rat to learn this maze?
How many attempts if we cut out its hippocampus?
What fraction of words can a subject remember?
What's the correlation between height and extraversion? …

9. Statistics Mathematical functions to be computed from data
Usually serves one of three functions:
Summarizes some aspect of the data (descriptive statistic)
Mean, median, maximum, quartiles, standard deviation, etc.
Estimates some parameter of the population (estimator)
Mean, standard deviation, etc. (almost always descriptive stat too)
Aids testing of some hypothesis about the population (inferential statistic)
Numerical value generally has no physical meaning
t, F, r, p, c2
Sample Size
Effect Size
Variability
Effect Size *
Sample Size
Variability
 “t”
t > 2  difference probably real; t<2  probably chance