course module 9: (some) statistics for managers who hate statistics
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Evidence-Based Management, Course Module 9: (Some) Statistics For Managers Who Hate StatisticsTRANSCRIPT
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Postgraduate Course
Evidence-Based Management
(Some) statistics for managers who hate statistics
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Postgraduate Course
Why do we need statistics?
1. How does my population look like?
2. Is there a difference?
3. Is there a model that ‘fits’?
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Postgraduate Course
Some statistics
Some statistic terms
1. Sample vs population
2. Variables
3. Levels of measurement
4. Central tendency
5. Hypothesis
Some statistic models
6. Mean
7. Variance, standard deviation
8. Confidence intervals
9. Statistical significance
10. Statistical power
11. Effect sizes 12.Critical appraisal
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Postgraduate Course
1. Sample vs population
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Postgraduate Course
Sample vs population
We want to know about these(population: N)
We have to work with these(sample: n)
population mean: μ
selection
sample mean: X _
statistics
fit?
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Postgraduate Course
Law of large numbers
The larger the sample size (or the number of observations), the more accurate the predictions of the characteristics of the whole population, and smaller the expected deviation in comparisons of outcomes.
As a general principle it means that, in the long run, the average (mean) of a large number of observations will be close to (or: may be taken as the best estimate of) the 'true mean’ of the population.
Sample vs population
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Postgraduate Course
Sample size: why does it matter?
Law of the large numbers: a reliable and accurate representation of the population
Statistical power: to prevent a type 2 error / false negative
Sample vs population
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Don’t confuse: representativeness and reliability
The sample size has no direct relationship with
representativeness; even a large random sample can be
insufficiently representative.
Postgraduate Course
Sample vs population
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Postgraduate Course
2. VariablesPostgraduate Course
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Postgraduate Course
VariablesPostgraduate Course
Variable: anything that can be measured and can differ across entities or time
Independent variable: predictor variable (value does not depend on any other variables)
Dependent variable: outcome variable (value depends on other variables)
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Postgraduate Course
3. Level of measurementPostgraduate Course
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Postgraduate Course
Level of measurementPostgraduate Course
Relationship between what is being measured and the numbers that represent what is being measured.
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Postgraduate Course
Categorical
Continuous
Nominal
Ordinal
Interval
Ratio
Level of measurement
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Postgraduate Course
Nominal scale
Classification of categorical data. There is no order to the values, they are just given a name (‘nomen’) or a number. The numbers can’t be used to calculate … (you can’t calculate the mean of fruit) .. only frequencies
1 = Apples2 = Oranges3 = Pineapples4 = Banana’s5 = Pears6 = Mango’s
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Postgraduate Course
Ordinal scale
Classification of categorical data. Values can be rank-ordered, but the distance between the values have no meaning. The numbers can only be used to calculate a modus or a median
1. Full Professor2. Associate professor3. Assistant professor4. PhD5. Master6. Bachelor
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Postgraduate Course
Interval scale
Classification of continuous data. Values can be rank-ordered, and the distance between the values have meaning. However, there is no natural zero point
1. John (1932)2. Denise (1945)3. Mary (19524. Marc (1964)5. Jeffrey (1978)6. Sarah (1982)
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Postgraduate Course
Ratio scale
Classification of continuous data. Values can be rank-ordered, the distance between the values have meaning and there is a natural zero point.
1. Jeffrey (192 cm)2. John (187 cm)3. Sarah (180 cm4. Marc (179 cm)5. Mary (171 cm)6. Denise (165 cm)
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Postgraduate Course
Nominal Ordinal Interval Ratio
Classification Yes Yes Yes Yes
Rank-order No Yes Yes Yes
Fixed and equal intervals No No Yes Yes
Natural 0 point No No No Yes
Nominal Ordinal Interval Ratio
Mode Yes Yes Yes Yes
Median No Yes Yes Yes
Mean No No Yes Yes
Levels of measurement
Categorical Continuous
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Postgraduate Course
Levels of measurement
Ordinal or interval? Can I calculate a mean?
Q3: Every organization is unique, hence the findings from scientific research are not applicable.
☐ Strongly agree
☐ Somewhat agree
☐ Neither agree or disagree
☐ Somewhat disagree
☐ Strongly disagree
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Postgraduate Course
4. Central tendency
The aim is to find a single number that characterises the typical value of the variable in the sample. Which one you use depends in part on the level of measurement of the variable.
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Postgraduate Course
Central tendency
Central tendency of a set of data / numbers(what number is most representative of the dataset / population?)
7, 9, 9, 9, 10, 11,11, 13, 13
Mean = 10,2
Median = 10
Mode = 9
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Postgraduate Course
Central tendency
Central tendency of a set of data / numbers(what number is most representative of the dataset / population?)
3, 3, 3, 3, 3, 3, 100
Mean = 16,9
Median = 3
Mode = 3
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Postgraduate Course
5. Hypothesis
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Postgraduate Course
“It is easy to obtain evidence in favor of virtually any theory,
but such ‘corroboration’ should count scientifically only if it
is the positive result of a genuinely ‘risky’ prediction, which
might conceivably have been false.
… A theory is scientific only if it is refutable
by a conceivable event. Every genuine test
of a scientific theory, then, is logically an
attempt to refute or to falsify it.”
Hypothesis: falsifiability
Carl Popper
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Postgraduate Course
Hypothesis
Null hypothesis (H0): Big Brother contestants and members of the public will not differ in their scores on personality disorder questionnaires
Alternative hypothesis (H1): Big Brother contestants will score higher on personality disorder questionnaires than members of the public.
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Postgraduate Course
Hypothesis: type I vs type II error
null hypothesis is true
& was rejected(type I error)
α
null hypothesis is false
& was rejected(correct conclusion)
null hypothesis is true
& was accepted(correct conclusion)
null hypothesis is false
& was accepted(type II error)
β
H0 is true H0 is false
reject H0
accept H0
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Postgraduate Course
Statistic models
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Postgraduate Course
Statistic models: prediction
likely not likely
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Postgraduate Course
6. The mean
The most widely used statistic model
μX_
or
sample population
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Postgraduate Course
The mean
EBMgt Lecturer
Num
ber o
f Frie
nds
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Postgraduate Course
The mean
Assessing the fit of the mean
Sum of squared errors (SS): (-1,6) + (-0,6) + (0,4) + (0,4) + (1,4) = 5,2
Variance (s ): = = 1,3
Standard deviation (s): √s = 1,14
2 2 2 2 2
2 SSN-1
5,24
2
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Postgraduate Course
The second most widely used statistic model
σs or
sample population
7. Standard Deviation
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Postgraduate Course
Standard Deviation
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Postgraduate Course
110IQ
Postgraduate Course
Standard Deviation
Which class would you prefer to teach?
130 170
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Postgraduate Course
110 130IQ
S=10
S=20
S=60
170
Postgraduate Course
Standard Deviation
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Postgraduate CoursePostgraduate Course
Standard Deviation
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Postgraduate CoursePostgraduate Course
So, what does
“two standard deviations of the mean”
mean?
Standard Deviation
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Postgraduate Course
8. Confidence intervalsPostgraduate Course
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Postgraduate Course
A confidence interval gives an estimated range of values which is likely to include an unknown population parameter (e.g. the mean).
Confidence intervals are usually calculated so that this percentage is 95% (95% CI)
Confidence intervals
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Postgraduate Course
When you see a 95% confidence interval for a mean, think of it like this: if we’d collected 100 samples and calculated the mean for each sample, than for 95 of these samples the mean would fall within the confidence interval.
Confidence intervals
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Postgraduate Course
1,96!
Confidence intervals
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Postgraduate Course
Confidence intervals
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Postgraduate Course
2008 2009
4,5
4,0
3,5
5,0
3,0
“According to the federal government, the unemployment rate has dropped from 4.3% to 3.8%.”
95% CI= 4,1 - 3,5.
This means the unemployment rate could have increased from 4.0 to 4,1 !
Confidence intervals
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Postgraduate Course
When a point estimate (e.g. mean, percentage) is given, always check:
standard deviation
or
confidence interval
Confidence intervals
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Postgraduate Course
9. Statistical significance
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Postgraduate Course
Statistical significance
Sir Ronald A. Fisher1890 - 1962
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Significant = the probability of incorrectly rejecting the null hypothesis (= Type I error, α)
p = 0,05 / p = 0,01
Postgraduate Course
Statistical significance
(1 in 20 / 1 in 100)
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Postgraduate Course
Statistical significance
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110 130
Postgraduate Course
1. Is there a difference / an effect?
2. How certain is it that the difference / effect found is not a chance finding?
X_
0 X_
1
Statistical significance
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Testing multiple hypothesis
When you test 20 different hypotheses (or independent variables), there is a high chance that at least one will be
statistically significant.
example:
Does apples, bacon, cheese, eggs, fish, garlic, hazelnuts, ice cream, ketchup, lamb, melons, nuts, oranges, peanut butter, roasted food, salt, tofu, vinegar, wine or yoghurt cause cancer?
Postgraduate Course
Statistical significance
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Significance testing:
always prospective, never retrospective
Postgraduate Course
Statistical significance
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Statistical significant ≠ practical relevant
Postgraduate Course
Effect size
Statistical significance
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Postgraduate Course
10. Statistical power
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Sample size Effect size (Significant increase in IQ)
4 10
25 4
100 2
10.000 0,2
Postgraduate Course
Statistical power
The statistical power: the power to detect a meaningful effect, given sample size, significance level, and effect size.
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Postgraduate Course
Overpowered: sample size too large, high probability of making a Type I error
Underpowered: sample size too small, high probability of making a Type II error.
Statistical power
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Postgraduate Course
11. Effect size
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Postgraduate Course
Effect size
Effect size: a standardized measure of the magnitude of effect, independent of sample size
standardized > makes it possible to compare effect sizes across different studies that have measured different variables, or have used different scales of measurement
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Postgraduate Course
Effect sizes
Cohen’s d
Pearson’s r
other - Hedges’ g
- Glass’ Δ
- odds ratio OR
- relative risk RR
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Postgraduate Course
Effect sizes
Cohen’s d
Effect size based on means or distances between/among means
Interpretation
< .10 = small
.30 = moderate
> .50 = large
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Postgraduate Course
Effect sizes
Pearson’s r
Effect size based on ‘variance explained’
Interpretation
< .10 = small (explains 1% of the total variance)
.30 = moderate (explains 9% of the total variance)
> .50 = large (explains 25% of the total variance)
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Postgraduate Course
12. Critical appraisal
When you critically appraise a study, what characteristics of the findings will you consider to determine its statistical significance and magnitude?
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Postgraduate Course
Critical appraisal
When you critically appraise a study, what characteristics of the findings will you consider to determine its statistical significance and magnitude?
p-value
confidence interval
sample size / power
effect size
practical relevance