 # Becoming Acquainted With Statistical Concepts CHAPTER CHAPTER 12.

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Becoming Acquainted With Statistical Concepts CHAPTER CHAPTER 12

Statistics is an objective way of interpreting a collection of observations. Why We Need Statistics Types of statistics -Descriptive techniques -Correlational techniques -Differences among groups Univariate and multivariate

Frequently used in offices, labs, and homes for statistical analysis How Computers Are Used in Statistics Types of software for statistics -Biomedical Series (BIMED) -Statistical Analysis System (SAS) -Statistical Package for the Social Sciences (SPSS)

Central tendency scores Measures of Central Tendency and Variability -Mean: Average -Median: Midpoint -Mode: Most frequent Variability scores -Standard deviation -Range of scores

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

Normal Curve

Skewness

Kurtosis

What statistical techniques tell us Statistics -Reliability (significance) of effect -Strength of the relationship (meaningfulness) Types of statistical techniques -Relationships among variables -Differences among groups

Probability Interpreting Statistical Findings -Alpha: false positive (type I error) Typical: p <.05 or p <.01 -Beta: false negative (type II error) Meaningfulness (effect size) Power: Probability of rejecting the null hypothesis when it is false

Truth Table for the Null Hypothesis H 0 true Correct decision Type I error (alpha) H 0 false Type II error (beta) Correct decision Accept Reject

Alpha & Beta Alpha = p-level in statistical tests 1 - Beta = the power of the statistical test

Ways to  Statistical Power  alpha (often preset to.05 or.01)  beta (often preset to.20)  N

Statistical Power and Effect Size Effect size is invariant Overpower = greater N than needed to statistically detect the effect (detect trivial effects) Underpower = not enough N to statistically detect the effect (can’t detect meaningful effects) Appropriate statistical power is achieved from an a priori power analysis

Power Analysis Effect size = statistical power With the info of effect size, alpha, and beta, power analysis can tell us what N we need for the study Tables, computer programs, and math equations