A Reading Guide to Intuitive Biostatistics
Preface
1
“Statistics & Probablity Are Not Intuitive”
Commentary
Vocabulary
Motulsky vocab
Additional vocab
Key functions
Chapter Notes
1.1
We Tend to Jump to Conclusions
1.2
We Tend to Be Overconfident
1.3
We see Patterns in Random Data
1.4
We don’t realize that coincidences are common
1.5
We don’t expect variability to depend on sample size
1.6
We Have Incorrect Intuitive Feelings About Probability
1.7
We Find it Hard to Combine Probabilities
1.8
(We Avoid Thinking About Ambiguous Situations)
1.9
We Don’t Do Bayesian Calculations Intuitively
1.10
We are Fooled By Multiple Comparisons
1.11
We tend to ignore alternative explanations
1.12
We are fooled by regression to the mean
1.13
We let our biases determine how we interpret data
1.14
We crave certainty, but statistics offers probability
1.15
Further reading
1.16
References
1.17
Annotated Bibliography
1.17.1
Multiple comparisons
2
“The complexities of probability”
Commentary
2.1
Focal parts of chapter
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
2.2
Basics of probability
2.3
Probability as long-term frequency
2.3.1
Probabilities as predictions from a model
2.3.2
Probabilities based on data
2.4
Probabilities As Strength of Belief
2.4.1
Subjective probabilities
2.4.2
“Probabilities” used to quantify ignorance
2.4.3
Quantitative predictions of one-time events
2.5
Calculations with probabilities can be easier if you switch to calcualting with whole numbers
2.6
Common Mistakes: Probability
2.6.1
Mistake: Ignoring assumptions
2.6.2
Mistake: Trying to understand probability without clearly defining both the numerator & the denominator
2.6.3
Mistake: Reversing probability statements
2.6.4
Mistake: Believing the probability has a memory
2.7
Lingo
2.7.1
Probability vs. odds
2.7.2
Probability vs. statistics
2.7.3
Probability vs. likelihood
2.8
Probability In Statistics
2.9
Further reading
2.9.1
References
2.9.2
Annotated Bibliography
3
“From sample to popluation”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
3.1
[ ] “”
3.2
[ ] “”
3.3
[ ] “”
Further reading
References
4
“Confidence Interval of a Proportion”
Preamble
On proportions, frequencies, and percentages
On counting “events” versus counting things
On confidence intervals versus p-values
Vocabulary
Motulsky vocab
Additional vocab
Chapter Notes
4.1
“Data Expressed as Proportions”
4.2
“The Binomial Distribution: From Population to Sample”
4.3
“Example: Free Throws in Basketball”
4.4
(“Example: Deaths of Premature Babies”)
4.5
“Example: Polling Voters”
4.6
“Assumptions: Confidence Interval of a Proportion”
4.6.1
“Assumption: Random (or representative) sample”
4.6.2
“Assumption: Independent observations”
4.7
“Assumption: Accurate Data”
4.8
“What Does 95% Confidence Really Mean”
4.8.1
“A simulation”
4.8.2
“95% Change of What?”"
4.8.3
“What is Special About 95%”
4.8.4
(“What If The Assumption Are Violated”)
4.9
“Are You Quantifying the Event You Really Care About?”
4.10
Lingo
4.10.1
CI versuse confidence limits
4.10.2
Estimate
4.10.3
Confidence level.
4.10.4
Uncertainty Interval
4.11
“Calculating The CI of a Proportion”"
4.11.1
“Several methods are commonly used”"
4.11.2
“How to compute the modified Wald method by Hand”
4.11.3
Shortcut for proportion near 50% (OPTIONAL)
4.11.4
Shortcut for proportion far from 50% (OPTIONAL)
4.11.5
Shortcut when the numerator is zero: The rule of three (OPTIONAL)
4.12
“Ambiguity if The Proportion Is 0% or 100%”
4.13
“An Alternative Approach: Bayesian Credible Intervals”
4.14
“Common Mistakes: CI of A Proportion”
4.14.1
“Mistake: Using 100 as the denominator when the value is a percentage”
4.14.2
“Mistake: Computing binomial ICs from percentage change in a continous variable”
4.14.3
“Mistake: Computating a CI from data that look like a proportion but really is not”
4.14.4
“Mistake: Interpretting a Bayesin credible interval wihtout knowing what prior probabilities (or probabilitiey distribuiton) were assumed for the analysis”
4.15
Q & A
5
“Confidence Interval of Survival Data”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
5.1
[ ] “”
5.2
[ ] “”
5.3
[ ] “”
Further reading
References
6
“COnfidence interval of Counted Data (Poisson Distribution)”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
6.1
[ ] “”
6.2
[ ] “”
6.3
[ ] “”
Further reading
References
7
“Graphing Continous Data”
Commentary
Vocabulary
Motulsky vocab
Additional vocab
Key functions
Chapter Notes
7.1
“Continuous Data”
7.2
“The Mean and Median”
7.3
“Lingo: Terms used to Explain Variability”
7.3.1
“Biological variability”
7.3.2
“Precision”
7.3.3
“Bias”
7.3.4
“Accuracy”
7.3.5
“Error”
7.4
“Percentiles”
7.5
“Graphing Data to Show Variation”
7.5.1
“Scatter plots”
7.5.2
“Box-and-whiskers plots”
7.5.3
“Violin plots”
7.6
“Graphing Distributions”
7.6.1
“Frequency distributions”
7.6.2
“Cumulative frequency distribution” (OPTIONAL)
7.7
“Beware of Data Massage”
7.7.1
“Beware of filtering out impossible values”
7.7.2
“Beware of adjusting data”
7.7.3
“Beware of smoothing”
7.7.4
“Beware of variable that are the ratio of two measurements”
7.7.5
“Beware of normalized data”
Beware of ratios of ratios
7.8
Q & A
Further reading
References
Annotated Bibliography
8
Chapter “Types of Variables”
Commentary
Vocabulary
8.0.1
Motulsky vocab
8.0.2
Aditional vocab
8.0.3
Key functions
Chapter Notes
8.1
“Continous Variables”
8.1.1
“Interval variables”
8.1.2
“Ratio variables”
8.2
“Discrete Variables”
8.2.1
“Ordinal variables”
8.2.2
“Nominal and binomial variables”
8.3
“Why It Matters”
8.4
“Not Quite As Distinct As They Seem”
8.5
Q&A
Further reading
References
Annotated Bibliography
9
Chapter “Quantifying Scatter”
Commentary
Vocabulary
9.0.1
Motulsky vocab
9.0.2
Aditional vocab
9.0.3
Key functions
Chapter Notes
9.1
“Interpretting A Standard Deviation”
9.2
How It Works: Calculating SD"
9.3
“Why n-1?”
9.3.1
“When it somes makes sense to use n in the denominator”
9.3.2
“Why it usually makes sense to use n-1 in the denominator”
9.3.3
“The fine print”
9.4
“Situations in Which n Can Seem Ambiguous”
9.4.1
“Replicate measurements within repae experiments”
9.4.2
“Eyes, ears and elbos”
9.4.3
“Representaitve experiments”
9.4.4
“Trials with one subject”
9.5
“SD and Sample Size”
9.6
“Other Ways to Quantify & Display Variability”
9.6.1
“Coefficient of variation”
9.6.2
“Variance”
9.6.3
“Interquartile range”
9.6.4
“Five-number smmary”
9.6.5
“Median absolute deviation”
9.7
Q&A
Further reading
References
Annotated Bibliography
10
“The Gausian Distribution”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
10.1
[ ] “”
10.2
[ ] “”
10.3
[ ] “”
Further reading
References
11
“The Lognormal Distribution and Geometric Mean”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
11.1
[ ] “”
11.2
[ ] “”
11.3
[ ] “”
Further reading
References
12
“Confidence Interval of a Mean”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
12.1
[ ] “”
12.2
[ ] “”
12.3
[ ] “”
Further reading
References
13
“The Theory of Confidence Intervals”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
13.1
[ ] “”
13.2
[ ] “”
13.3
[ ] “”
Further reading
References
14
“Error Bars”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
14.1
[ ] “SD VERSUS SEM”
14.1.1
[ ] “What is the SD”
14.1.2
[ ] “What is the SEM?”
14.1.3
[ ] “The SEM does not quanitfy variablity among variables”
14.1.4
[ ] “The SEM quantifies how precisely you know the population mean”
14.1.5
[ ] “How to compute the SD from the SEM”
14.2
[ ] “WHICH KIND OF ERROR BAR SHOULD I PLOT”
14.2.1
[ ] “Goal: To Show the variation among the values”
14.2.2
[ ] “Goal: To show how precisely you have determined the population mean”
14.2.3
[ ] “Goal: To create persuaive propaganda”
14.3
[ ] “THE APPEARANCE OF ERROR BARS”
14.4
[!] “HOW ARE SD AND SEM RELATED TO SAMPLE SIZE”"
14.4.1
[ ] “If you increase the sample size, is the SEM expected to get larger, get smaller, or stay about the same?”
14.4.2
[ ] “If you increase the sample size, is the SD expected to get larger, get smaller, or stay about the same?”
14.5
“GEOMETRIC SD ERROR BARS” (SKIP)
14.6
[ ] “COMMON MISTAKES: ERROR BARS”
14.6.1
[ ] “Mistake: Plotting mean & error bar instead of plotting a frequency distribution”
14.6.2
[ ] “Mistake: Assuming that all distributions are Gaussian”
14.6.3
[ ] “Mistake: Plotting a mean & error bar w/o defining how the error bars were computed.”
14.7
[ ] Q&A
Further reading
References
Annotated Bibliography
15
“Introducing P-Values”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
15.1
[ ] “”
15.2
[ ] “”
15.3
[ ] “”
Further reading
References
16
“Statistical Significance and Hypothesis Testing”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
16.1
[ ] “”
16.2
[ ] “”
16.3
[ ] “”
Further reading
References
17
“COmparing Groups with Confidence Intervals and P Values”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
Chapter Notes
17.1
[ ] “”
17.2
[ ] “”
17.3
[ ] “”
Further reading
References
References
Published with bookdown
A Reading Guide to Intuitive Biostatistics
Chapter 11
“The Lognormal Distribution and Geometric Mean”
Commentary
Vocabulary
Motulsky vocab
Aditional vocab
Key functions
None
Chapter Notes
11.1
[ ] “”
11.2
[ ] “”
11.3
[ ] “”
Further reading
References