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Chapter 1

# PSYB07H3 Chapter Notes - Chapter 1: Exploratory Data Analysis, Statistical Inference, John Tukey

by OC13004

Department

PsychologyCourse Code

PSYB07H3Professor

Dwayne PareChapter

1This

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Chapter 1

IMPORTANT TERMS

- random sample: ensures that each and every element of the population has an equal chance of

being selected; ex. Drawing names from a hat

- randomly assign: which subjects are part of which groups

- population sample random sample random assignment

- population: the entire collection of events in which you are interested; ex. All the self-esteem

scores of all high school students in the US – the collection of all the students’ self-esteem

scores would form a population

- since the populations are usually too large to measure, we draw a sample of observations from

that population and use it to infer something about the characteristics of the population

- external valididty: whether the sample reflects the population to which it is intended to make

inferences

- non-random sample: intended to closely reflect what would be obtained with a truly random

sample

- one person’s sample may be another one’s population

- random assignment is concerned with internal validity: ensures that the results obtained are

the results of the differences in the way the groups were treated, not a result of who happened

to be placed in the groups; ex. if all timid ppl were put in one group, and all assertive ppl put in

the other, the results would likely be a function of group assignment, and less so about the

difference in treatment

- random assignment is usually far more important than random sampling

- variable: a property of an object or event that can take on different values; ex. hair colour is a

variable because it is a property of an object (hair) and can take on different values (brown,

yellow, red, grey etc.)

- independent variable: the controlled variable

- dependent variable: the data

- the study is about the independent variables, and the results of the study (the data) are the

dependent variables

- discrete variables: can take on only a limited number of values; ex. gender

- continuous variables: can assume any value between the lowest and highest points on the

scale; ex. age

- quantitative data (aka measurement data): the results of any sort of measurement, usually

measured with some sort of instrument; the interest is “how much” of some property a

particular object represents; ex. peoples’ weights, grades on a test

- categorical data (aka frequency data or qualitative data): categorizing things where frequencies

exist for each category; ex. the categories of “highly anxious”, “neutral”, and “low anxious”

DESCRIPTIVE AND INFERENTIAL STATISTICS

- descriptive statistics: when the purpose is to merely describe the data

- exploratory data analysis (EDA): created by John Tukey; it is necessary to pay close attention to

the data and examine it in detail before invoking more technically involved procedures

- inferential statistics: since it is not possible to examine a whole population, we draw samples

from that population and create inferences about the whole population

- parameter: a measure that refers to the entire population

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