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Chapter 1

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


Department
Psychology
Course Code
PSYB07H3
Professor
Dwayne Pare
Chapter
1

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PSYB07 Sept 14/2012
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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