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chapter 6

chapter 6. Becoming Acquainted With Statistical Concepts. Chapter Outline. Why we need statistics Description and inference are not statistical techniques Ways to select a sample Justifying post hoc explanations Measures of central tendency and variability

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chapter 6

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  1. chapter6 Becoming Acquainted With Statistical Concepts

  2. Chapter Outline • Why we need statistics • Description and inference are not statistical techniques • Ways to select a sample • Justifying post hoc explanations • Measures of central tendency and variability • Basic concepts of statistical techniques

  3. Why We Need Statistics • Statistics is an objective way of interpreting a collection of observations. • Types of statistics • Descriptive techniques • Correlational techniques (relationship) • Differences among groups (comparison)

  4. Descriptive Statistical Techniques • Measures of Central Tendency • Mean, median, mode • Variability Scores • Standard deviation, range of scores • Frequency Distributions • Distribution of scores including the frequency with which they occur • Frequency Intervals • Small ranges of scores within a frequency distribution into which scores are groups

  5. Correlational Statistical Techniques • Pearson Product Moment Coefficient of Coorelation • Also known as Pearson’s r, interclass correlation, or simple correlation • Measure of the degree of association between two variables • Ranges from -1 to 1 • Indicates the “relationship” between two variables • To be discussed in Chapter 8

  6. Example of Correlation

  7. Comparison Statistical Techniques • t Test • Measure of the difference(s) between two groups • Compare t obtained from your data with a value from a t table • To be discussed in Chapter 9

  8. Description and Inference • Not statistical techniques • Any statistic describes the sample of participants for which it was calculated • If the sample represents some larger group (population), then the findings can be inferred to the larger group ***The statistic has nothing to do with inference***

  9. Ways to Select a Sample • Random sampling (selection) • Tables of random numbers • Stratified random sampling • Categorize population prior to random selection • Systematic sampling • Appropriate for large samples • Random assignment • Groups are formed within a sample

  10. Justifying Post Hoc Explanations • A post hoc explanation may only compare the characteristics measured within the sample • To be able to generalize to the population, random selection is necessary • “Plausible”

  11. Measures of Central Tendencyand Variability • Central tendency scores • Mean: average • Median: midpoint • Mode: most frequent • Variability scores • Standard deviation • Range of scores

  12. Categories of Statistical Tests • Parametric • Normal distribution • Normal curve • Skewness • Kurtosis • Equal variances • Independent observations • Nonparametric (distribution free) • Distribution is not normal

  13. Normal Curve

  14. Skewness

  15. Kurtosis

  16. Statistics • What statistical techniques tell us • Reliability (significance) of effect • Strength of the relationship (meaningfulness) • Types of statistical techniques • Relationships between or among variables • Regression • Correlation • Differences between or among groups • t test • ANOVA

  17. Summary • The type of statistic used does not determine whether the findings can be generalized; rather, it is sampling that permits (or limits) inference • Statistics can do two things: • Establish significance • A relationship or difference is reliable (may expect it to happen again if the study were repeated) • Assess meaningfulness • Importance of the results

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