The document discusses inferential statistics, focusing on its goals, methods, and significance testing. Key statistical tests, including t-tests, chi-squared tests, and correlation, are explained alongside methodologies for analyzing data sets and interpreting results. Additionally, it outlines case studies on the effectiveness of an information literacy course through descriptive statistics and hypothesis testing.

1. INFERENTIAL STATISTICS

2. GOALS OF SERIES
Comfort
Fears

3. SESSION OBJECTIVES
Purpose of Inferential Statistics
Probability
Elements of Significance Testing
Three key tests
• T-test
• Chi-squared
• Correlation (or binomial)
Effect Measures

4. PURPOSE OF INFERENTIAL STATISTICS
• Infer results
• Draw conclusions
• Increase the
Signal-Noise ratio
Signal
Noise

5. INFERENTIAL STATISTICS
Tests of hypotheses
• Expectations
• Associations
Accounts for uncertainty
• Random error
• Confidence interval

6. HYPOTHESES
Your
Hypothesis
(H1)
Null
Hypothesis
(H0)

7. NOT TO PROVE, BUT TO FALSIFY
H1
Difference
H0
No
Difference

8. NOT TO PROVE, BUT TO FALSIFY
H1
>=10%
Increase
H0
<10%
Increase

9. REVIEW OF DESCRIPTIVE
STATISTICS

10. LEVELS OF MEASUREMENT (NOIR)
Nominal
•Counts by category
•Binary (Yes/No)
•No meaning
between the
categories (Blue is
not better than
Red)
Ordinal
•Ranks
•Scales
•Space between
ranks is subjective
Interval
•Integers
•Zero is just another
value – doesn’t
mean “absence
of”
•Space between
values is equal and
objective, but
discrete
Ratio
•Interval data with a
baseline
•Zero (0) means
“absence of”
•Space between is
continuous
•Includes simple
counts

11. Central
Tendency
ErrorSpread
DESCRIPTIVE
STATISTICAL ANALYSIS

12. CENTRAL TENDENCY BY
LEVELS OF MEASUREMENT
Interval
or Ratio
Mean
Median
Nominal
or Rank
Mode
Median
(rank only)

13. SPREAD
Interval &
Ratio
•Range
•Quantiles
•Standard
Deviation
Nominal &
Rank
•Distribution
Tables
•Bar Graphs
How variable is the data?

14. RANGE &
QUARTILES

15. FORMULAS
Mean Standard Deviation

16. PROBABILITY
WHAT’S PROBABILITY GOT TO DO WITH STATISTICS?

17. WHAT IS PROBABILITY?
Chance of something happening (x)
Expressed as P(x)=y
Between 0 and 1
Based on distribution of events

18. STEM-AND-LEAF
Stem Leaf
0 0111222222222222223333334444555666
6677788899
1 0000000011122223333356778899
2 00122234444799
3 0245
Groups Last
digit
Years at UNT
0 5 13
1 6 13
1 6 13
1 6 13
2 6 15
2 6 16
2 7 17
2 7 17
2 7 18
2 8 18
2 8 19
3 11 29
4 11 29
4 12 30
4 12 32
4 12 34
5 12 35
5 13

19. Stem Leaf Count
0 1122223334445555666666677777899 31
1 000011122222222333346677889 27
2 0122234468 10
3 1112355888 11
4 12 2
Range Count
0-9 31
10-19 27
20-29 10
30-39 11
40-49 2
0
10
20
30
40
0-9 10-19 20-29 30-39 40-49
Histogram of Years at UNT

20. NORMAL DISTRIBUTIONS

21. PROBABILITY DISTRIBUTION

22. Set the mean to 0
Standard Deviations above and
below the mean

23. DEMONSTRATION OF DISTRIBUTIONS
Distribution of the
Population
The “Truth”
N is the # of samples
n is the number of
items in each sample
Watch the cumulative mean & medians
slowly merge to the population

24. ACTIVITIES

25. CASE STUDY
• Background: Info-Lit course is meeting
resistance from skeptical faculty.
• Research Questions:
• Does the IL course improve grades on final
papers?
• Can the IL course improve passing rates for the
course?
• Do students in different majors respond differently
to the IL training?
• Is the final score related to the number of credit
hours enrolled for each student?

26. METHODOLOGY
Selection
• Two sections of same course
with different instructors.
• Random Assignment
Outcome
• Blinded scoring by 2 TAs
• Scores range from 1-100
• Passing grade: 70

27. ACTIVITIES
Table 1
• Distribution
of scores
Table 2
• Distribution
of passing
rates by
major
Table 3
• Correlation
of scores
with credit
hours

28. DESCRIPTIVE STATISTICS
OF CASE STUDY

29. DISTRIBUTION OF SCORES
Table 1
•Distribution of
scores
Table 2
•Distribution of
passing rates
by broad
field of major
Table 3
•Correlation of
scores &
credit hours

30. SIGNIFICANCE TESTING

31. SIGNIFICANCE TESTING
• Groups against each other
• A group against the population or
standard
Comparing
significance of
differences
• Risk of being wrong
• Alpha (α)
• Set in advance
What is
“significant”?
• The value that the statistic must meet
or exceed to be statistically significant.
• Based on statistic and α
Critical Value

32. STEPS IN SIGNIFICANCE TESTING
Which
Test?
Calculate
Statistic
Critical
Value of
Statistic?
Probability
(p-value)

33. KEY ELEMENTS OF SIGNIFICANCE
TESTING
Null Hypothesis
Measure of Central Tendency
Standard deviations
Risk of being wrong (alpha)
• Usually .05 or .025 or .01 or .001
Degrees of freedom (df)

34. DEGREES OF FREEDOM
Number of values in
the final calculation
of a statistic that
are free to vary.

35. DEGREES OF FREEDOM EXPLAINED
• All these have a
mean of 5:
• 5, 5, 5
• 2, 8, 5
• 3, 2, 10
• 7, 4, & ?
• If 2 values are
known and the
mean is known,
then the 3rd value is
also known.
• Only 2 of the 3
values are free to
vary.

36. CALCULATING DEGREES OF FREEDOM
(DF)
For a single sample:
• Degrees of freedom (df) for t-test = n-1
For more than one group:
• df=∑(n-1) for all groups (k)
• OR, ∑ n-k
For comparing proportions in categories (k):
• df= ∑k-1 (# of categories minus 1)

37. COMPARING VALUES
T-TEST

38. T-TEST
Used with interval or ratio data
Based on normal distribution
Four Decisions
• Paired or un-paired samples?
• Equal or unequal variances (standard deviations)?
• Risk?
• One- or two-tail?
• Direction of expected difference
• Best to bet on difference in both directions (2-tail)

39. One-Tail
Two-Tail

40. T-TEST FORMULA FOR UNPAIRED
SAMPLES
𝑡 =
𝑥1 − 𝑥2
𝑆 𝑥1−𝑥2
Signal
Noise
Difference Between Group Means
𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝐺𝑟𝑜𝑢𝑝𝑠

41. ELEMENTS OF T-TEST USING EXCEL
DATA ANALYSIS TOOLPAK
•UnpairedPaired or Unpaired
samples?
•Equal*Equal or Unequal
Variances?
•Data for intervention group
•Data for control group
Data
•0Hypothesized
difference
•0.025 (for a 2-tail test)
Alpha

42. T-TEST IN EXCEL

43. READING T-TEST RESULTS
∑(n-1) =
(51-1)+(50-1) =
50+49=99
<=0.025?

44. IS THE DIFFERENCE SIGNIFICANT?
p=0.0005

45. TESTING DISTRIBUTION OF
NOMINAL DATA

46. PEARSON’S CHI-SQUARED (Χ2)
GOODNESS OF FIT TEST
Does an observed frequency distribution
differ from an expected distribution
• Observed is the sample or the intervention.
• Expected is the population or the control or a
theoretical distribution.
• Will depend on your Null Hypothesis
Nominal or categorical data
• Counts by category

47. EXPECTED RATIOS FOR CASE STUDY
Research Question:
•Do students in different majors
respond differently to the IL training?
Null Hypothesis
•The ratio of students who passed will
be the same for all majors.

48. WHEN TO USE PEARSON’S CHI-
SQUARED GOODNESS OF FIT TEST
Nominal Data
Sample Size
• Not too large:
• Sample is at most 1/10th of population
• Not too small:
• At least five in each of the categories
for the expected group.

49. OBSERVED PASSING RATES BY MAJOR
Major Passed Not Passed Grand Total
Arts 6 7 13
Humanities 8 5 13
Social
Sciences 17 10 27
STEM 20 5 25
Undeclared 16 7 23
Total 67 34 101

50. EXPECTED RATIOS OF PASSING RATES
BY MAJOR
• H0: Rates of passing will be the same for all majors.
• Expected rates: 70% of class passes.
• Expected ratios: 70% of each major passes.
Major Passed Not Passed Grand Total
Arts 11.2 (16*.7) 4.8 16
Humanities 11.2 (16*.7) 4.8 16
Social
Sciences 18.2 (26*.7) 7.8 26
STEM 16.1 (23*.7) 6.9 23
Undeclared 14 (20*.7) 6 20

51. CHI-SQUARED GOF TEST FORMULA
•χ2
=
𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑−𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 2
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑
• Critical value of Chi-squared depends
on degrees of freedom.
• Degrees of freedom
• Based on the number of categories or
table cells (k)
• df=k-1

52. CHI-SQUARED IN EXCEL
What is Null
Hypothesis?
There is no difference between the majors regarding
passing rates.
What is your
alpha (risk)?
0.05
Data in a
summary
tables?
Actual Ratios
Expected Ratios
Excel
function:
=CHISQ.TEST(actual range1,expected range2)
Provides a
p-value
0.0000172
Is p-value
<= alpha?
Yes

53. CORRELATION OF SCORE &
SEMESTER HOURS ENROLLED

54. STATISTICAL CORRELATION
Quantitative value of relationship of 2 variables
•-1 represents a perfect indirect correlation
•0 represents no correlation
•+1 represents a perfect direct correlation
Expressed in range of -1 to +1
•How much two variables change together
Based on co-variance

55. PEARSON’S PRODUCT MOMENT
CORRELATION COEFFICIENT
Most commonly used
statistic
Normally distributed
interval or ratio data only
Labeled as r
Multiplication =
Interaction
Signal
Noise
𝑟𝑥𝑦 =
𝑥 − 𝑥 𝑦 − 𝑦
𝑛 − 1 𝑠 𝑥 𝑠 𝑦

56. CORRELATION IN EXCEL
•No correlation
Null Hypothesis?
•=PEARSON(range1,range2)
Coefficient function (r):
Does NOT have a single function to test for
significance
Calculate Probability:
n # in sample 101
df # in sample - 2 99
alpha 0.025 for 2-Tail Test 0.025
r =PEARSON(range1,range2) 0.362287
t =r*SQRT(alpha)/SQRT(1-r^2) 3.867434
p =T.DIST.2T(t,df) 0.000197

57. CORRELATIONS FOR ORDINAL DATA
Spearman’s ϱ (rho)
•Use if there are limited ties in
rank.
Kendall’s τ (tau)
•Use if you have a number of ties.

58. SELECTING THE TESTS

59. KNOW THE TESTS
Assumptions
Limitations
Appropriate data type
What the test tests

60. FACTORS ASSOCIATED WITH CHOICE
OF STATISTICAL METHOD
Level of
Measurement
What is being
compared
Independence
of units
Underlying
variance in the
population
Distribution Sample size
Number of
comparison
groups

61. USE A FLOW CHART

62. GOING BEYOND THE P-
VALUE
EFFECT SIZES

63. AND THE P-VALUE SAYS…
Much about
the distributions
More about
the H0 than H1
Little about size
of differences

64. MORE USEFUL STATISTICS
Effect Sizes
•Tell the real story
Confidence Intervals
•State your certainty

65. EFFECT SIZES OF QUANTITATIVE DATA
Differences from
the mean
• Standardized
• weighted against the
pooled (average)
standard deviation
• Cohen’s d
Correlations
• Cohen’s guidelines for
Pearson’s r
• r = 0.362
Effect Size r>
Small .10
Medium .30
Large .50
𝑑 =
𝑥1 − 𝑥2
𝑠 𝑥1,𝑥2
𝑑 =
79.47 − 69.56
11.8036
𝑑 = 0.8392

66. EFFECT SIZES OF QUALITATIVE DATA
Based on
Contingency
table
• Uses probabilities
• 𝑅𝑅 =
𝑎 𝑎+𝑏
𝑐 𝑐+𝑑
Relative risk
• 𝑅𝑅 =
41∗65
10∗36
•RR = 1.608
•The passing rate for the intervention
group was 1.6 times the passing rate for
control group.
RR of Case
Study
Pass No Pass Total
Intervention a (41) b (24) a+b (65)
Control c (26) d (10) c+d (36)
Totals a+c (67) b+d (34) a+b+c+d (101)

67. CONFIDENCE INTERVALS
Point estimates
Intervals
Based on
Expressed as:
•Single value
•Mean
•Degree of uncertainty
•Range of certainty around the
point estimate
•Point estimate (e.g. mean)
•Confidence level (usually .95)
•Standard deviation
•The mean score of the students
who had the IL training was 79.5
with a 95% CI of 76.4 and 82.5.

68. CASE STUDY CONCLUSIONS
• Research Questions:
• Could the IL course improve grades on final
papers?
• Could the IL course improve passing rates for the
course?
• Do students in different majors respond differently
to the IL training?
• Is the final score related to the number of credit
hours enrolled for each student?
• Control for external variables

69. STATISTICAL ANALYSIS
Signal
Noise