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Week 9 Psychology notes:

by: Regan McGillick

Week 9 Psychology notes: Psychology 100 (

Regan McGillick

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About this Document

Scientific research: descriptive and inferential statistics
Lori Bica
Class Notes
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This 2 page Class Notes was uploaded by Regan McGillick on Saturday April 2, 2016. The Class Notes belongs to Psychology 100 ( at University of Wisconsin - Eau Claire taught by Lori Bica in Spring 2016. Since its upload, it has received 15 views. For similar materials see INTRO TO PSYCHOLOGY in Psychlogy at University of Wisconsin - Eau Claire.


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Date Created: 04/02/16
Week 9 Psyc: Scientific Research: (3/28, 3/30.4/1) I. Statistical Methods: -why is this important? II. Two types of data: a. Quantitative data = numerical values (only focus on this type) i. Descriptive statistics ii. Inferential statistics b. Qualitative data = characteristics III. Descriptive Statistics: used to describe or summarize sets of data to make it more understandable. a. Ex: study of Schizophrenia: symptoms: delusions and hallucinations A. Types of Descriptive Statistics: a. Measurers of Central Tendency: mean, median, mode b. Measurers of Variability: how similar/different: Range and Standard deviation c. Correlation Coefficients: looking at the relationship of the values: Strong or Weak Ex: Schizophrenia: looking at the relationship between variables: #of people in family that have this disorder Schizophrenia: -variable 1: severity of symptoms : delusions, hallucinations -variable 2: # in a family Strong positive relationship between 2 variables +1.00 No relationship 0 Strong Negative relationship (inverse) - 1.00 Correlation Coefficients: continued -.85 -the value that tells about the strength of the relationship ( .85) -tells of the directing of the graph (negative or positive) (inverse or together) IV. Inferential Statistics: procedures for looking at probability that research results could derive from chance alone -you want your results to be real not just chance -inferential statistics enable researchers to be confident in drawing conclusion from their data A. Reading values: a. “p” = probability b. “r” = mean c. “t ” = analysis when wanting to compare means of 2 groups Ex: r = .60 = descriptive statistics (the strength and direction of 2 variables) p= .01 = inferential statistics (how confident are coefficient is in the experiment)  p value determines how confident can be in the r value  r value (mean) describes the degree of difference  p value tells the significance of he degree of difference


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