Basic Statistical Concepts

1. Basic Statistical Concepts www.phdcomics.com

2. So, you have collected your data … • Now what? • We use statistical analysis to • test our hypotheses • make claims about the population • This type of analyses are called inferential statistics

3. But, first we must … • Organize, simplify, and describe our body of data (distribution). • These statistical techniques are called descriptive statistics

4. Distributions • Recall a variable is a characteristic that can take different values • A distribution of a variable is a summary of all the different values of a variable • Both type (each value) and token (each instance)

5. Distribution How excited are you about learning statistical concepts? 1 2 3 4 5 6 7 Comatose Hyperventilating 1 2 2 3 4 4 5 6 7 7 Types: 1,2,3,4,5,6,7 9 Tokens: 1,2,2,3,4,4,5,6,7

6. Distribution 2 1 1 2 3 4 5 6 7 N = 9

7. Properties of a Distribution • Shape • symmetric vs. skewed • unimodal vs. multimodal • Central Tendency • where most of the data are • mean, median, and mode • Variability (spread) • how similar the scores are • range, variance, and standard deviation

8. Representing a Distribution • Often it is helpful to visually represent distributions in various ways • Graphs • continuous variables (histogram, line graph) • categorical variables (pie chart, bar chart) • Tables • frequency distribution table

9. Distribution • What if we collected 200 observations instead of only 9? …

10. Distribution N = 200 50 40 30 20 10 1 2 3 4 5 6 7

11. Continuous Variables

12. Categorical Variables

13. Frequency Distribution Table

14. Shape of a Distribution • Symmetrical (normal) • scores are evenly distributed about the central tendency (i.e., mean)

15. Shape of a Distribution • Skewed • extreme high or low scores can skew the distribution in either direction Negative skew Positive skew

16. Shape of a Distribution • Unimodal • Multimodal Minor Mode Major Mode

17. Distribution • So, ordering our data and understanding the shape of the distribution organizes our data • Now, we must simplify and describe the distribution • What value best represents our distribution? (central tendency)

18. Central Tendency • Mode: the most frequent score • good for nominal scales (eye color) • a must for multimodal distributions • Median: the middle score • separates the bottom 50% and the top 50% of the distribution • good for skewed distributions (net worth)

19. Central Tendency • Mean: the arithmetic average • add all of the scores and divide by total number of scores • This the preferred measure of central tendency (takes all of the scores into account) population sample

20. Computing a Mean 10 scores: 8, 4, 5, 2, 9, 13, 3, 7, 8, 5 ξΧ = 64 ξΧ/n = 6.4

21. Central Tendency • Is the mean always the best measure of central tendency? • No, skew pulls the mean in the direction of the skew

22. Central Tendency and Skew Mode Median Mean

23. Central Tendency and Skew Mode Median Mean

24. Distribution • So, central tendency simplifies and describes our distribution by providing a representative score • What about the difference between the individual scores and the mean? (variability)

25. Variability • Range: maximum value – minimum value • only takes two scores from the distribution into account • easily influenced by extreme high or low scores • Standard Deviation/Variance • the average deviation of scores from the mean of the distribution • takes all scores into account • less influenced by extreme values

26. Standard Deviation • most popular and important measure of variability • a measure of how far all of the individual scores in the distribution are from a standard (mean)

27. Standard Deviation low variability small SD high variability large SD

28. Computing a Standard Deviation 10 scores: 8, 4, 5, 2, 9, 13, 3, 7, 8, 5 ξΧ/n = 6.4 8 – 6.4 = 4 – 6.4 = 5 – 6.4 = 2 – 6.4 = 9 – 6.4 = 13 – 6.4 = 3 – 6.4 = 7 – 6.4 = 8 – 6.4 = 5 – 6.4 = 1.6 - 2.4 - 1.4 - 4.4 2.6 6.6 - 3.4 0.6 1.6 - 1.4 2.56 5.76 1.96 19.36 6.76 43.56 11.56 0.36 2.56 1.96 SS = 96.4 10.71 3.27

29. Standard Deviation • In a perfectly symmetrical (i.e. normal) distribution 2/3 of the scores will fall within +/- 1 standard deviation -1 +1 3.13 6.4 9.67

30. Variance vs. SD • So, SD simplifies and describes the distribution by providing a measure of the variability of scores • If we only ever report SD, then why would variance be considered a separate measure of variability? • Variance will be an important value in many calculations in inferential statistics

31. Review • Descriptive statistics organize, simplify, and describe the important aspects of a distribution • This is the first step toward testing hypotheses with inferential statistics • Distributions can be described in terms of shape, central tendency, and variability • There are small differences in computation for populations vs. samples • It is often useful to graphically represent a distribution