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Chapter 1

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

by OC1062122

Department

PsychologyCourse Code

PSYB07H3Professor

Dwayne PareChapter

1This

**preview**shows page 1. to view the full**5 pages of the document.**Textbook Notes B07 September 14, 2016

Chapter #1 Lec 2

Chapter #1 – Basic Concepts

1.1– Important Terms

- Stress management program was designed for high-school students but would be

impossible to apply it to the population of all high-school students because:

oThere are too many students

oMakes no sense to apply a program to everyone until we know whether it is a

useful program

-Random sample: ensures each and every element in the population has an equal chance

of being selected

oExample: putting numbers into a hat and drawing blindly)

-Randomly assign: half of the subjects to a group will receive the treatment and half will

not

-Population: the entire collection of events (students’ scores, people’s incomes, rats’

running speeds, e.t.c.) in which you are interested

oThe point is that populations can be of any size.

- we are forced to draw only a sample of observations from that population and to use

that sample to infer something about the characteristics of the population.

-Sample: Set of actual observations. Subset of the population.

- Randomness has 2 aspects to be considered:

oExternal validity: the question of whether the sample reflects the population

othe other aspect concerns Random assignment

Internal validity: The degree to which a study if logically sound and free

of confounding variables.

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

othe definition of a population depends on what you are interested in studying.

- Random assignment is far more important than random sampling

-Variable: a property of an object or event that can take on different values.

oFor example, hair color is a variable because it is a property of an object (hair)

and can take on different values (brown, yellow, red, gray, etc.).

oIndependent variable: is controlled by the experimenter (receive the treatment)

Can be either quantitative or qualitative and discrete or continuous

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Textbook Notes B07 September 14, 2016

Chapter #1 Lec 2

oDependent Variable: The variable being measured. The data or score.

Generally (but not always) quantitative and continuous

-Discrete variables: limited number of values or take on a small set of possible values

oSuch as gender, high-school class

-Continuous variables: take on any value. Any value between the lowest and highest

points on the scale

oSuch as age and self-esteem scores

-Quantitative data (measurement data): the results of any sort of measurement

oGrades on a test, people’s weights, scores on a scale of self-esteem

oSome instrument has been used to measure something and we are interested in

“how much”

-Categorical data (frequency data or qualitative data): categorizing thing, and our data

consists of frequencies for each category

oThere are 34 females and 26 males in our study, 15 people were classes as highly

anxious and 33 as neutral and 12 as low anxious

1.2– Descriptive and Inferential Statistics

- There are two primary divisions of the field of statistics that are concerned with the use

we make of these data.

oWhenever our purpose is merely to describe a set of data, we are employing

descriptive statistics.

Descriptive statistics: Statistics which describe the sample data without

drawing inferences about the larger population.

For example, one of the first things we want to do with our data is

to graph them, to calculate means (averages) and other measures,

and to look for extreme scores or oddly shaped distributions of

scores

they are primarily aimed at describing the data.

Twenty- five years ago John Tukey developed what he called exploratory

statistics, or exploratory data analysis (EDA).

Exploratory data analysis (EDA): A set of techniques developed by Tukey

for presenting data in visually meaningful ways.

oAfter we have described our data in detail and are satisfied that we understand

what the numbers have to say on a superficial level, we will be particularly

interested in what is called inferential statistics.

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