Basic statistics
Elementary Concepts in Statistics
Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one's general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of those concepts can be found in statistical textbooks. Recommended introductory textbooks are: Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).
What are variables?
Correlational vs. experimental research
Dependent vs. independent variables
Measurement scales
Relations between variables
Why relations between variables are important
Two basic features of every relation between variables
What is "statistical significance" (p-value)
How to determine that a result is "really" significant
Statistical significance and the number of analyses performed
Strength vs. reliability of a relation between variables
Why stronger relations between variables are more significant Why significance of a relation between variables depends on the size of the sample
Example: "Baby boys to baby girls ratio"
Why small relations can be proven significant only in large samples
Can "no relation" be a significant result?
How to measure the magnitude (strength) of relations between variables
Common "general format" of most statistical tests
How the "level of statistical significance" is calculated
Why the "Normal
Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one's general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of those concepts can be found in statistical textbooks. Recommended introductory textbooks are: Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).
What are variables?
Correlational vs. experimental research
Dependent vs. independent variables
Measurement scales
Relations between variables
Why relations between variables are important
Two basic features of every relation between variables
What is "statistical significance" (p-value)
How to determine that a result is "really" significant
Statistical significance and the number of analyses performed
Strength vs. reliability of a relation between variables
Why stronger relations between variables are more significant Why significance of a relation between variables depends on the size of the sample
Example: "Baby boys to baby girls ratio"
Why small relations can be proven significant only in large samples
Can "no relation" be a significant result?
How to measure the magnitude (strength) of relations between variables
Common "general format" of most statistical tests
How the "level of statistical significance" is calculated
Why the "Normal