PSY347 Study Guide - Final Guide: George Bernard Shaw, Standardized Test, Observer-Expectancy Effect
CHAPTER ONE –
Statistics – is the study of how to collect, organise, analyse and interpret numerical
information from data
Inductive statement – statement whose truth is assessed by collecting and analysing data
• The term truth has different meanings depending on its context
• Inductive statements are based on statistical reasoning
• Example: the more hours you spend in the library, the better your academic
performance
Statistical Reasoning: Tools used to evaluate inductive statements
Behavioural science has developed 2 specialities that are part of the rational inductive
process:
1. Research design
2. Statistical reasoning
Research design – the science of collecting data, making observations about the real world,
considering how many observations to make and under what conditions to make them
Statistical reasoning begins with the collected data and prescribes the rules by which
rational statements about those data can be made
Research design and statistical reasoning are intertwined
Pygmalion effect (also known as experimenter bias): people at i aodae ith othes
expectations
• People tend to do better when treated as if they are capable of success
• You a get hat ou epet
• Observed by Eliza Doolittle & George Bernard Shaw
Psychologist Robert Rosenthal studied if the Pygmalion effect exists in different settings
• Pygmalion effect applies in the rat lab – rat handlers expect quick learning → they
get quick learning
Oak School – adiisteed Haad Test of Ifleted Auisitio HTIA hih as atuall
a standardised test of intelligence called Tests of General Ability (TOGA)
• Intellectual growth – post-test IQ – pretest IQ (+ve scores suggest increase in IQ)
• Pygmalion effect exists in the classroom
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The Pygmalion Effect, known as experimenter bias, is now an accepted fact that can affect
research resign. To combat, research studies can use:
Placebo – in a drug study, a substance that looks like the drug being tested but actually has
no effect
Blind Design – not knowing which subjects are assigned to which experimental condition
Single blind → either the participants or the research does not know
Double blind → both the participants and the research does not know
The experimental/statistical examination of the Pygmalion effect → led to important social
consequences
This example (drug study) shows 3 characteristics of the experimental/statistical method:
1. The inspiration for an experiment is a pre-experimental observation
Example: Shaw observed characteristic of human nature and Rosenthal and Jacobson seek
to verify observation
2. Statistical tools must distinguish between samples and population
3. It must allow us to derive meaningful results from data that are not perfectly
consistent –that fluctuate from one person/situation to another
Statistical toolbox must include methods to measure the differences b/w individuals
Samples from populations:
Population – all the members of the group under consideration
Sample – a subgroup of a population under consideration
Statistic – refer to any measurement on a sample – e.g. sample mean → statistic
Parameter – any measured characteristic of a population – e.g. population mean →
parameter
Sample statistics are typically used to estimate population parameters
• Descriptive statistics → to describe our samples
• Inferential statistics → generalise from our samples to the wider population
Measuring all the elements of a population is:
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• Costly
• Difficult
• Impossible
Hence, samples are drawn b/c:
• Cheaper
• Quicker
• More convenient
1 of the most important questions in stats: What can we say about a population when all we
know about is a sample?
Types of Sampling:
Random sample – every member of a population has an equal chance of being elected
• Unbiased representative sample BUT time-consuming
Stratified sampling – dividing the target population into important subcategories, and
selecting the members who have the most characteristics that represent the population
• Time-consuming
• Large effort to make sample representative
Volunteer sampling (also called self-selecting) – individuals who have chosen to be involved
in a study.
• Participants receive an incentive
• Can be unrepresentative
Opportunity sampling – simply selecting those people that are available at the time
• Quick and economical
• Very unrepresentative and often biased by researcher
Probability:
Random – unpredictable given our current knowledge
• Data always contains random effects
Probability theory – science that deals with the nature of randomness
Probability – a measure of our ignorance or uncertainty about the outcomes of events in the
world
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Document Summary
Statistics is the study of how to collect, organise, analyse and interpret numerical information from data. Inductive statement statement whose truth is assessed by collecting and analysing data: the term truth has different meanings depending on its context, example: the more hours you spend in the library, the better your academic. Inductive statements are based on statistical reasoning performance. Statistical reasoning: tools used to evaluate inductive statements. Behavioural science has developed 2 specialities that are part of the rational inductive process: research design, statistical reasoning. Research design the science of collecting data, making observations about the real world, considering how many observations to make and under what conditions to make them. Statistical reasoning begins with the collected data and prescribes the rules by which rational statements about those data can be made. Psychologist robert rosenthal studied if the pygmalion effect exists in different settings: pygmalion effect applies in the rat lab rat handlers expect quick learning they get quick learning.
