1. SFB stats workshop
Bodo Winter

2. usefulness

3. learning outcomes

4. Two learning curves

5. Two learning curves

6. Two learning curves
feel free to ask me on questions BOTH regarding R and regarding stats

7. Plan for today
Introductory remarks Describing data Introduction to R Inferential stats (t-test, Chi-Square test)
Wednesday: Linear models

8. Course website ?

9. Math-assisted thinking
“Statistics, more than most other areas of mathematics, is just formalized common sense, quantified straight thinking.”
Paulos (1992: 58)
Paulos, J. A. (1992). Beyond numeracy: Ruminations of a numbers man. New York: Vintage Books.

10. Publish paper, data and scripts Preprocessing/ Data Preparation
The Research Cycle
Theory/Hypothesis
Publish paper, data and scripts
Data collection
Write-up
ALL OF THAT IS STATISTICS
Preprocessing/ Data Preparation
Statistical Analysis

11. Publish paper, data and scripts Preprocessing/ Data Preparation
The Research Cycle
Theory/Hypothesis
Publish paper, data and scripts
Data collection
Write-up
ALL OF THAT IS STATISTICS
Preprocessing/ Data Preparation
Statistical Analysis

12. Statistics = “getting meaning from data” Michael Starbird
Descriptive Statistics
Inferential Statistics
COGNITIVE TOOL ...
a way to assist thinking ... from this perspective follow certain things ... chiefly that if you do some type of stats that you don’t fully understand then you are essentially working against the purpose of doing statistics
Michael Starbird

14. p < 0.001 M = 120.5 Hz male SD = 9.39 Hz M = 209 Hz female
Descriptive Stats
M = Hz
male
SD = 9.39 Hz
M = 209 Hz
female
Inferential Stats
SD = Hz
p < 0.001

15. Describing distributions

16. Describing distributions
1 throw

17. Describing distributions
2 throws

18. Describing distributions
3 throws

19. Describing distributions
4 throws

20. Describing distributions
5 throws

21. Describing distributions
6 throws

22. Describing distributions
7 throws

23. Describing distributions
8 throws

24. Describing distributions
30 throws
frequency distribution, probability distribution

25. inspired by the Cartoon Guide to statistics

26. Uniform Distribution
inspired by the Cartoon Guide to statistics

27. “Gaussian” Normal Distribution
inspired by the Cartoon Guide to statistics

28. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

29. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

30. 200 Response Times

31. 200 Response Times
Mean =

32. 200 Response Times
Mean =

33. 200 Response Times
Mean =

34. 200 Response Times
Mean =

35. 200 Response Times
Mean =

36. Voice pitch of 100 men
Voice pitch of 100 women

37. The mean is a balance point The median is a half-way point

38. The mean is a balance point The median is a half-way point

39. The mean is a balance point The median is a half-way point

40. The mean is a balance point The median is a half-way point

41. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

42. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

43. Voice pitch of 100 men
Voice pitch of 100 women
Variance = 97.08
Variance =

44. How to calculate the variance
Raw Data 6 3 2 5 4

45. How to calculate the variance
Raw Data
Mean of Data 6 4 3 2 5

46. How to calculate the variance
Raw Data
Mean of Data
Differences 6 4 2 3 -1 -2 5

47. How to calculate the variance
Raw Data
Mean of Data
Differences
Squared Differences 6 4 2 3 -1 1 -2 5

48. How to calculate the variance
Raw Data
Mean of Data
Differences
Squared Differences 6 4 2 3 -1 1 -2 5
sum this and divide by N-1 to get variance

49. Formula for the variance
taking the sum
sum of squares
squared differences from the mean
dividing by total number of values minus one

50. Formula for the variance
“sum of squares”

51. Voice pitch of 100 men
Voice pitch of 100 women
Variance = 97.08
Variance =

52. Voice pitch of 100 men
Voice pitch of 100 women
SD = 9.85
SD = 19.65

53. Variance
Standard deviation

54. 68% of the data lie within 1 standard deviation of the mean

55. Next time you read a paper...
Between what values do you expect 68% of the data? What about 95% of the data?

56. The normal distribution family

57. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

58. Ways continuous distributions differ
Location
Spread
Shape
Mean
Standard deviation

59. Normal Distribution
inspired by the Cartoon Guide to statistics

60. A distribution with positive skew
inspired by the Cartoon Guide to statistics

61. A distribution with negative skew
inspired by the Cartoon Guide to statistics

62. reproducible, open research
free, open-source, platform-independent
ever-growing community
STUDENTS!!! you would think SPSS is easier for students ... I beg to differ; I myself had no programming experience and found SPSS highly unintuitive plus you are increasing the threshold to actually use the software by having it restricted to university computers
students are MUCH more likely to use something that they can run on their own computer
and although they find R daunting at first, they quickly feel like doing something super fancy because what they do LOOKS and FEELS like programming (it isn’t)
if you don’t believe me: I’ve taught R in content classes (a class on gesture) and even to literature students and it worked
fully-fledged programming language

64. R packages on CRAN

65. base package versus tidyverse
Hadley Wickham
base package versus tidyverse

66. Approaching R: Having the right attitude
“I have been writing R code for years, and every day I still write code that doesn’t work!”
Wickham & Grolemund (2017: 7)
Wickham, H. & Grolemund, G (2017). R for Data Science. Sebastopol, CA: O’Reilly.

67. STUDENTS. you would think SPSS is easier for students
STUDENTS!!! you would think SPSS is easier for students ... I beg to differ; I myself had no programming experience and found SPSS highly unintuitive plus you are increasing the threshold to actually use the software by having it restricted to university computers
students are MUCH more likely to use something that they can run on their own computer
and although they find R daunting at first, they quickly feel like doing something super fancy because what they do LOOKS and FEELS like programming (it isn’t)
if you don’t believe me: I’ve taught R in content classes (a class on gesture) and even to literature students and it worked