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Chapter 1- Introduction to Statistics
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1
Why do we need Statistics?
To help researchers make sense of gathered information, organize and summarize data, communicate findings, and justify conclusions.
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2
Definition of Statistics
A set of methods for organizing, summarizing, and interpreting data, providing order out of chaos.
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3
Descriptive statistics
Methods that summarize, organize, and simplify data.
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4
Inferential statistics
Methods that use sample data to make general statements about a population.
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5
Population vs. Sample: Population
Set of all individuals of interest in a particular study.
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6
Population vs. Sample: Sample
Set of individuals selected from the population intended to represent the population.
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7
Variables
Characteristics or conditions that change or have different values across individuals.
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8
Nominal Scale
A set of categories without a natural order (e.g., academic majors, gender).
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9
Ordinal Scale
Categorized ranked observations (e.g., freshman, sophomore, junior, senior).
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10
Interval Scale
Ordered categories with equal intervals but no true zero point (e.g., temperature).
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11
Ratio Scale
Interval scale with an absolute zero point allowing for real differences (e.g., height, weight).
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12
Discrete variables
Variables that are separate and indivisible (e.g., number of letters in your name).
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13
Continuous variables
Variables that can be divided into an infinite number of possibilities (e.g., weight, distance).
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14
Sampling Error
The estimated error between data from a sample and the population intended to be studied.
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15
Independent Variable
Variable that you manipulate or categorize in a study.
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16
Dependent Variable
Variable that you measure; its value depends on the independent variable.
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17
Confounding Variable
Variable that systematically varies with the independent variable; should be controlled.
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18
Constructs
Hypothetical concepts used to define behavior, needing operational definitions to be measured.
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19
Reliability
The consistency of a measure over time.
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20
Validity
The extent to which a test measures what it is supposed to measure.
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21
Hypothesis Testing
The process of drawing conclusions about whether relations between variables are supported or not.
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22
Correlational Method
Observes whether two different variables are related; correlation does NOT equal causation.
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23
Experimental Method
Determines cause and effect by manipulating independent variables and randomly assigning participants.
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24
Between-groups designs
Different participants complete tasks, and comparisons are made between groups.
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25
Within-groups designs
The same participants do the tasks more than once, and comparisons are made over time.
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26
Quasi-Experimental Method
Compares groups not created by manipulating an independent variable, determined by participant variables.
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