Stats- Chapter 1

Stats- Chapter 1 - Chapter 1 Introduction Key Terms:...

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Chapter 1 Introduction
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Key Terms: Population vs. Sample.  Population: the complete set of data or  observations for one study Sample: a subset of a population Example: Adults’ memory may decrease with age
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Key Terms: Barack Obama wants you to find out how  many registered voters in Philadelphia  support his economic stimulus.  You  randomly select and contact 1,000 people  from the list of registered voters and  determine that 66% (660 people) support  the plan. What is the population of interest? What is the sample?
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Key Terms: Parameter vs. Statistic  parameter  describes a population, an  index of population statistic  describes a sample
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Key Terms: Inferential statistics. vs. Descriptive  statistics  Inferential statistics: to draw a conclusion  about conditions of a population from the  study of a sample A statistic attempts to predict a parameter, but it  is rarely 100% accurate.  Descriptive statistics: to organize and  summarize data or observations
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Key Terms: Descriptive or Inferential? Of 350 randomly selected people in the town of Luserna,  Italy, 280 people had the last name Nicolussi   80% of 350 randomly selected people had the last name Nicolussi 80% of all people in Italy had the last name Nicolussi The last four semesters an instructor taught  Intermediate Algebra, the following numbers of people  passed the class: 17, 19, 4, 20 During the last 4 semesters, an average of 15 people passed the  class The instructor will never pass more than 20 people in an  Intermediate Algebra class
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Application of Statistics in Research Experimental Studies : generally put  participants in treatment or control  conditions Allow inferences about cause-effect relations Use two types of variables: Independent  Dependent
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Types of Variables Independent Variable (IV):  treatment  manipulated by the experimenter Experimenter wants to know, ‘If I change X,  will it have an effect on Y?’ ‘X’ is the  independent  variable  The IV is specified in advance
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Types of Variables Dependent Variable (DV):  The outcome  measure that is observed/counted by the  experimenter Experimenter wants to know, ‘If I change X,  will it have an effect on Y?’ ‘Y’ is the  dependent  variable The DV is not known in advance
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Types of Variables Confounding Variable:  uncontrolled variable  that compromises the interpretation of a study Experimental conditions should only vary in the  dimension of interest
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Experimental Study Example: Experimental Study on the Effect of One 
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This note was uploaded on 04/29/2010 for the course PSYCH 1167 taught by Professor Bingshi during the Spring '10 term at Temple.

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Stats- Chapter 1 - Chapter 1 Introduction Key Terms:...

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