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Wilfrid Laurier University
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Psychology
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PS296
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Max Gwynn
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Chapter 1&2

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Psychology

PS296

Max Gwynn

Winter

Description

Chapter 1: Intro
Statistics – set of procedures and rules for reducing large masses of data to
manageable proportions & for allowing us to draw conclusions from those data
Take statistics to be able to analyze the results of experimental research
1.1 – A Changing Field
researchers in behavioural sciences concerned with whether a difference that they
found b/w experimental groups (or relationship between 2+ variables) was reliable
field slowly changed by asking if a difference was meaningful
effect size – number of different indices of importance
combining studies dealing with a particular thing – meta-analysis
1.2 – Importance of the Context
common experimental task demonstrating morphine tolerance – placing rat on a
uncomfortably warm surface
latency of paw-lick is the measure of rat’s sensitivity to pain
Siegel’s Morphine Study
Conditioned (learned) responses
Unconditioned (natural) responses
Theorized – administering a series of pre trials develop morphine tolerance
Used the compensatory mechanism
Concluded – drug overdoses occur in novel settings
1.3 – Basic Terms statistical procedures = descriptive & inferential statistics
Descriptive Statistics
Describing a set of data
EX.
Average length of time it takes a normal mouse to lick its paw when placed on warm
surface
Time it takes a morphine-injected mouse to do same thing
Amount of change in latency of paw-licks
Crime rates, dieting scored on Eating Restraint Scale, summary info concerning exam
grades in a course
Inferential Statistics
Inferring characteristics of populations (parameters) from characteristics of samples
(statistics)
Generalizing from single observations
Used when studying something that has very little variability
Difference b/w how we determine the # of legs on a cow VS. the milk production of
cows depends on variability (would need a herd of cows to measure how much milk a
cow will produce, every cow is different)
Must draw sample from population
Populations, Samples, Parameters, Stats
Population – entire/complete set of events in which you are interested in
Can range from a relatively small set of #’s – easily collected, to an infinitely large set of
#’s – can never be collected completely
Usually interested in large populations
Sample – a subset from a population of events interested in Used to infer something about the characteristics of the population
Compute numerical values
Statistics – numerical values summarizing sample data
Parameters – numerical values summarizing population data
Random Sample – sample which each member of population has equal chance of
inclusion
True random sample – estimate parameters of population & get good idea of accuracy
of our estimates
A sample that is not random is meaningless – may no accurately reflect entire
population
Relevant Population – collection of numbers from which the sample has been
randomly drawn
Inference
Taking 2 different samples of mice and testing them, one sample mean would be larger
than another
Must make statistical inferences from a sample to a population, then must make a
logical inference
Want to know whether a difference we find is unlikely to be due to chance, but also
want to know how meaningful the difference is
Meta-analysis – drawing conclusions from a WHOLE set of similar experiments on a
given phenomenon
1.4 – Selection among Statistical Procedures
we must describe a set of data before making inferences
Decision Tree – scheme used selecting among the available statistical procedures to
be presented in the text
Graphical representation of decisions involved in the choice of statistical procedures
Located on the inside of the back cover of text Types of Data
Numerical Data
Measurement Data (Quantitative) – data obtained by measuring objects/events
Score on a measure of stress, person’s weight, speed @ which person can read a page
Categorical Data (frequency/count data) – data representing counts or number of
observations in each category
There were 238 votes for the new curriculum and 118 against it
Obtaining a latency score for each mouse (measurement data)
Classifying the mice as showing long, medium, short latencies, then counting umber in
each category (categorical data)
Differences Vs. Relationships
Most statistical questions fall into 2 overlapping categories
Differences & relationships
Number of Groups or Variables
Obvious distinction between statistical techniques concerns the # of groups/# of
variables
What is referred to as an independent t test – restricted to the case of data from @ most
2 groups of subjects
Analysis of variance – applicable to any number of groups
1.5 – Using Computers
most calculations now done by computers
simple procedures – formulae important in defining

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