# UW-Madison PSYCH 210 - Definitions and Basic Concepts (4 pages)

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## Definitions and Basic Concepts

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## Definitions and Basic Concepts

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Lecture number:
1
Pages:
4
Type:
Lecture Note
School:
Course:
Psych 210 - Basic Statistics for Psychology
Edition:
1
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Unformatted text preview:

PSYCH 210 1st Edition Lecture 1 Outline of Last Lecture I Overview of Syllabus II Dog Agility Video Clip a Class formulated Statistics questions Outline of Current Lecture I Descriptive vs Inferential Statistics II Populations and Samples III Terminology and Notation IV Types of Variables Current Lecture I Descriptive Statistics a Def procedures used to describe organize and summarize data b Most basic type of statistics no manipulation of data just what do we have c Ex Average course time of dog agility route i Average Mean which is a form of Central Tendency d Ex Most common type of faults dogs make i Most Common Mode form of Central Tendency e Percent of dogs in each height category i Percent Summary Statistic f What is the range of ages of dogs that compete i Range spread of scores Variability Statistic 1 Variability Statistics Include a Range b Standard Deviation c Variance g Is there a correlation between speed and accuracy These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute i Correlation II III Dogs YPS Yards per sec Number of Faults x y Inferential Statistics a Def Procedures that allow us to draw conclusions or make inferences that extend beyond the immediate data at hand b Useful for comparison of groups c Ex What is the Influence of the size of audience on dog performance Small Audience n 20 Large Audience n 20 Average Course time 45 s Average Course Time 36 s d Inference making larger population conclusion from small sample Populations vs Samples a Population Def The complete set of ALL individuals or measurements sharing some common observable characteristic b Sample Def a subset of observations drawn from a population i We use samples because unless the population is SUPER tiny entire populations are too big expensive etc ii Experiment Sample Conclusion Inference Whole Population iii We must rely on samples but we want to know about the entire

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