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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.

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:

-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

• 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

-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:

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%

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Population

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