行为科学统计精要-(1)[50页]课件.pptx
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1、Chapter 1: Introduction to StatisticsPowerPoint Lecture SlidesEssentials of Statistics for the Behavioral Sciences Eighth Editionby Frederick J Gravetter and Larry B. WallnauLearning Outcomes Know key statistical terms1 Know key measurement terms2 Know key research terms3 Know the place of statistic
2、s in science4 Understand summation notation5 Statistics requires basic math skills Inadequate basic math skills puts you at risk in this course Appendix A Math Skills Assessment helps you determine if you need a skills review Appendix A Math Skills Review provides a quick refresher course on those a
3、reas. The final Math Skills Assessment identifies your basic math skills competenceMath Skills Assessment1.1 Statistics, Science and Observations “Statistics” means “statistical procedures” Uses of Statistics Organize and summarize information Determine exactly what conclusions are justified based o
4、n the results that were obtained Goals of statistical procedures Accurate and meaningful interpretation Provide standardized evaluation procedures1.2 Populations and Samples Population The set of all the individuals of interest in a particular study Vary in size; often quite large Sample A set of in
5、dividuals selected from a population Usually intended to represent the population in a research studyFigure 1.1Relationship between population and sample Variables and Data Variable Characteristic or condition that changes or has different values for different individuals Data (plural) Measurements
6、or observations of a variable Data set A collection of measurements or observations A datum (singular) A single measurement or observation Commonly called a score or raw scoreParameters and Statistics Parameter A value, usually a numerical value, that describes a population Derived from measurements
7、 of the individuals in the population Statistic A value, usually a numerical value, that describes a sample Derived from measurements of the individuals in the sampleDescriptive & Inferential Statistics Descriptive statistics Summarize data Organize data Simplify data Familiar examples Tables Graphs
8、 Averages Inferential statistics Study samples to make generalizations about the population Interpret experimental data Common terminology “Margin of error” “Statistically significant”Sampling Error Sample is never identical to population Sampling Error The discrepancy, or amount of error, that exis
9、ts between a sample statistic and the corresponding population parameter Example: Margin of Error in Polls “This poll was taken from a sample of registered voters and has a margin of error of plus-or-minus 4 percentage points” (Box 1.1)Figure 1.2 A demonstration of sampling error Figure 1.3Role of s
10、tatistics in experimental research Learning Check A researcher is interested in the effect of amount of sleep on high school students exam scores. A group of 75 high school boys agree to participate in the study. The boys are A statisticA A variableB A parameterC A sampleDLearning Check - Answer A r
11、esearcher is interested in the effect of amount of sleep on high school students exam scores. A group of 75 high school boys agree to participate in the study. The boys are A statisticA A variableB A parameterC A sampleDLearning Check Decide if each of the following statements is True or False. Most
12、 research studies use data from samplesT/F When sample differs from the population there is a systematic difference between groupsT/FLearning Check - Answer Samples used because it is not feasible or possible to measure all individuals in the populationTrue Sampling error due to random influences ma
13、y produce unsystematic group differencesFalse1.3 Data Structures, Research Methods, and Statistics Individual Variables A variable is observed “Statistics” describe the observed variable Category and/or numerical variables Relationships between variables Two variables observed and measured One of tw
14、o possible data structures used to determine what type of relationship existsRelationships Between Variables Data Structure I: The Correlational Method One group of participants Measurement of two variables for each participant Goal is to describe type and magnitude of the relationship Patterns in t
15、he data reveal relationships Non-experimental method of studyFigure 1.4Data structures for studies evaluating the relationship between variables Correlational Method Limitations Can demonstrate the existence of a relationship Does not provide an explanation for the relationship Most importantly, doe
16、s not demonstrate a cause-and-effect relationship between the two variablesRelationships Between Variables Data Structure II: Comparing two (or more) groups of Scores One variable defines the groups Scores are measured on second variable Both experimental and non-experimental studies use this struct
17、ureFigure 1.5 Data structure for studies comparing groupsExperimental Method Goal of Experimental Method To demonstrate a cause-and-effect relationship Manipulation The level of one variable is determined by the experimenter Control rules out influence of other variables Participant variables Enviro
18、nmental variablesFigure 1.6 The structure of an experimentIndependent/Dependent Variables Independent Variable is the variable manipulated by the researcher Independent because no other variable in the study influences its value Dependent Variable is the one observed to assess the effect of treatmen
19、t Dependent because its value is thought to depend on the value of the independent variableExperimental Method: Control Methods of control Random assignment of subjects Matching of subjects Holding level of some potentially influential variables constant Control condition Individuals do not receive
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