1. Population: all possible members of the group you’re studying
Sample: subset of the population
Descriptive statistics measures of central tendency (mean, median, mode). Measures of variability
(range, standard deviation).
Inferential statistics- drawing conclusions (using ANOVA-for 3 samples or more; T-TEST-for 2 samples)
ANOVA- the analysis of variance statistic tests whether the means of more than two groups are equal
ALPHA= statistical significance =probability of a type 1 error (rejecting a null hypothesis when it is true)
Alternative (research) hypothesis- the hypothesis that we are testing, contrary to null hypothesis
Null hypothesis – the hypothesis that is of no scientific interest; sometimes the hypothesis of no difference
Type 2 error: power; accepting a null hypothesis when it is false; say no difference but actually is present
(missed)
-will be using 0.05% (5%)
NULL HYPOTHESIS NULL HYPOTHESIS
(TRUE) (FALSE)
REJECT TYPE 1 ERROR CORRECT
FAIL TO REJECT CORRECT TYPE 2 ERROR
Descriptive statistics: Data summarised in numerical form, such as mean, median, mode.:
Central tendency - Numbers that give some indication of the distribution of
data(mean,median,mode)
Measures of variability are numbers that indicate how spread out a set of scores is along a
distribution. Scores can be bunched up around the mean or spread out significantly along the
distribution. The three measures of variability are range, standard deviation, and variance.
Mode-the number that appears the most
Median- the middle number in a sorted list of numbers
Range- The area of variation between upper and lower limits on a particular scale
Standard deviation: the square root of a variance.

2. Variance- how far values lay from the mean; the square of standard deviation
Directional Hypothesis – one tailed test; results in one sample will be higher than the other. A n
alternative hypothesis that predicts that the results of one condition will be greater (or less) than another, rather than
a prediction that they will simply differ.
Non-directional hypothesis – two tailed test; results will differ in some way (could be negative or
positive)
Correlated samples- testing same individuals at different times; within subject ( all participants are
exposed to every treatment or condition.)
Independent samples- testing across individuals (groups) ; between subject
Non Empirical
Authority:
-Source of expert info/advice (God, government, parents)
-Can be wrong despite their convictions
-Plays diminished role in science
Logic:
-System/mode of reasoning
-Logical argument (deductive) if A+B are true= C follows
-Useful as statements/ arguments they’re based upon
-important to science, does not substitute for empirical evidence
Intuition:
-Spontaneous perception/ judgments (first impression)
-Common sense (shared attitudes, standard change over time, location)
Characteristics of science:
-Empirical (based on experiences)
-Objective (knowledge through observations; allows replication)
-Self-correcting (new evidence corrects previous beliefs)
Assumptions of Sciece:

3. -Realism: physical objects exist independently
-Rationality
-Regularity: world conforms to same universal laws nothing about human behavior falls outside of laws
nature
-Discoverability: belief that it’s possible to answer any questions through use of scientific methods
-Causality: belief all events are caused (determinism VS. free will)
-Discovery of regularities: describing phenomena; discovering laws
1) Law- statement that certain events are regularly associated w each other in an orderly way
2) Does not necessarily imply cause-effect; searching for causes
Development of theories:
1) Theory- statement explaining 1 or more laws:
-typically use at least 1 concept
-concept: thought/notion; not observed directly.
2) Theories must be falsifiable
Role of Theories:
1) Organize knowledge/ explain laws
- Good theories explain a large # of events + laws
2) Predict new laws
-discovery in one area opens up doors to others
3) guide research
4) Prediction + control
Hypothesis- statement assumed to be true for purpose of testing its validity
Operationalism- belief that scientific concepts must be defined in terms of observable observations.
- Concept must be tied to something that can be observed/ experienced directly
- Operational definition: precise description of a procedure used to empirically test a
theoretical concept

4. • Collect a few different measures when performing an experiment to be more accurate.
• Be able to manipulate the variable – better for accuracy
• Self report measures + observe the participants
• When measuring don’t generalize be specific
• Be very detailed in writing- to counteract questionable situations + repetition of
experiment/research
Chapter 5
Variables- an aspect of testing condition that changes w different conditions
- Operationalize theoretical concepts:
a) Concepts = intangible (abstract) [anger, happiness, hunger]
b) Variables = tangible [exam answers, level presses, # of customers served
Variable types:
1. Independent variables (IV) – condition manipulated (or selected) by experimenter to determine
its’ effects on behavior. Researcher must be able to control variable
-must have minimum 2 different levels (values)
-all other variables held constant
-subject variable
-typically used in correlational studies
2. Dependent variable (DV)- measure of subjects’ behavior that reflects the IVs’ effects. Can be
measured across several dimensions.
- Frequency: the # of times behavior is performed
- Rate: frequency relative to time (MPH, WPH)
-Duration: the # of time behavior lasts
-Latency: time btw instruction and initiation of behavior
-Topography: shape/style of behavior

5. -Force: intensity/ strength of behavior
-Locus: where the behavior occurs in the environment
Confounded Variable – one whose effects cannot be separated from the supposed IV
Covarying factors- can’t easily separate effects out
Other Variable Types:
• Quantitative: varies in amount ( age, # of lever presses)
• Categorical: varies in kind (college major, gender)
• Continuous: falls along continuum (not limited to a certain number of values; weight,
height)
• Discrete: falls into separate “bins” (no intermediate values possible; murders
committed, guests served)
Measurement:
Process of assigning numbers to events or objects according to a set of rules. Types of measurement
scales:
1. Nominal: divides into categories
2. Ordinal: ranks in order of magnitude (one category, different options)
3. Interval: differences btw numbers are meaningful
4. Ratio: has a meaningful zero
• Reliability- getting the same result across repeated measures of the same behavior
-Test-retest reliability: degree to which the same test score would be obtained on separate
occasions (SAT, GRE)
-Internal consistency: degree to which various items on a test measure the same thing (GRE,
sports trivia)
• Validity- the extent to which the experiment measures what it is supposed to
-measure should be reliable before it is valid
-Construct validity:

6. Does the test measure the theoretical constructs it’s supposed to and no others?
-Face validity:
Does the test appear to measure what it’s supposed to?
-Types of validity:
1) Content Validity:
Does the test sample the range of behavior represented by the concept being tested?
2) Criterion Validity:
Does the test correlate w other measures of the same construct? How well does our test
correlate with others?
3) Measurement error:
-Random error (aka error variance):
Variability associated with a consistent bias.
Ensure that all groups/conditions are equally affected by it, if it cannot be eliminated.
Total Variance (DV) = tx Variance (IV) + Relevant/Confound Variable (RV) +Random Error
(always alpha 0.05%)
Ex: Strength of connection = cable/router modem + # of devisec; distance; interferer + .05%