# Statistics

## I. Basic concepts.

1. Hypothesis testing (I). Null hypothesis. Alternative hypothesis.
It all spins around the null hypothesis.
2. Hypothesis testing (II). Alpha. Beta. Power. Sample size.
Size and power.
3. Hypothesis testing. Fragility index.
The fragility of the EmPress.
4. Confidence intervals.
Always look for an interval, but of confidence.
5. Confidence intervals. Prediction intervals. Tolerance intervals.
The poor relations.
6. Hypothesis contrast. Statistical significance. Tipo 1 error. Tipo 2 error.
To p or not to p…is that the question?
7. Statistical significance. Clinical relevance.
Even non-significant ps have a little soul.
8. The meaning of p-value.
Worshipped, but misunderstood.
9. Confidence intervals. Statistical significance. Clinical relevance.
Life is not rosy.
10. Inverse fallacy. Conditionals’ transposition fallacy.
The fallacy of small p.
11. Sample size.
Size does matter.
12. Sample size in surveys.
With very little we fine-tune a lot.
13. Sample size and precision.
Having a large n, who needs a samll p?.
14. Sample size calculation in survival studies.
By your actions they will judge you.
15. Study of qualitative variable dispersion.
Like a forgotten clock.
16. Location parameters. Arithmetic mean. Median. Mode. Geometric mean. Harmonic mean.
Virtue is the happy medium between two extremes, but…
17. Choosing between mean and median.
Meat or fish?
18. Robust location parameters.
A very robust family.
19. Robust scale parameters.
Do not be misled by the outliers.
20. Biased and unbiased estimators.
Why spare one?
21. Dispersion parameters. Variance. Standard deviation.
Not all deviations are perverse.
22. Dispersion parameters. Minimum. Maximum. Quartile. Decile. Percentile. Interquartile range.
The most wished statistical for a mother.
23. Standardization. z score. Frequiency distribution. Probability density curves. Histograms.
Give me a bar and I’ll move the earth.
24. Simmetry measures. Mean deviation. Distribution bias. Kurtosis.
Playing with powers.
25. Degrees of freedom. Sample size. Power.
Freedom in degrees.
26. Regression to the mean.
The curse.
27. Independency of variables. Paired data.
Independence matters.
28. Transforming data. Logarithm transformation. Inverse transformation.
Cheating Gauss.
29. Graphic representation of qualitative variable. Pie chart. Bar chart. Pareto’s graph.
Columns, sectors, and an illustrious italian.
30. Histogram. Bar graph.
As and egg to a chestnut.
31. Box plot.
A box with whiskers.
32. Quantile graph. q-q plot.
Some comparisons are not odious.
33. Circle graph.
Do not eat too many pies.
34. Outliers.
Black sheep.
35. Frequentist vs Bayesian’s statistics.
Rioja vs Ribera?

## II. Probability.

1. Normal distribution. Standardization.
The most famous of bells.
2. Stdent’s t distribution. Chi-squared distribution. Snédecor’s F distribution.
The big family.
3. Binomial probability (I). Binomial distribution. Bernouilli’s events.
The cooker and his cake.
4. Binomial probability (II). Survey errors. Lie. Obfuscation factor.
Lie to me.
5. Binomial probability (III). Calculating number of successes.
Do not gamble.
6. Conditioned probability. Bayes’ rule.
The stigma of guilt.
7. Conditioned probability. Bayes’ theorem.
8. Conditioned probability (II). Bayes’ rule.
9. The problem of birthday.
The deception of intuition.
10. Probabilitu value when numerator equals zero.
When nothing bad happens, is everything okay?

## III. Statistic tests.

1. Hypothesis testing. One-tailed test. Two-tailed test.
The tails of p.
2. Statistical significance. Power. Type I error. Type II error.
The false coin.
3. Multiple comparisons. Type I error.
Even a blind donkey…
4. Statistical test by variable type.
Brown or blond, all bald.
5. Contingency tables. Residuals. Pearson’s residuals. Standardized residuals. Adjusted residuals. Chi-square.
6. Chi-square test (I). Goodness of fit.
Solomonic decisions.
7. Chi-square test (II). Homogeneity test.
Counting sheep.
8. Chi-square test (III). Dependent variables.
Studying or working?.
9. Chi-squared-test (IV). Calulating expected values.
The why and wherefores.
10. Comparison of two proportions. Chki-square.
11. Confidence interval for a mean.
The error of confidence.
12. Concordance. kappa’s interobserver concordance coeficient.
A good agreement?.
13. Concordance. Bland-Altaman’s method.
Another stone with which nor to trip over.
14. Stratification. Mantel-Haenszel’s test. Confusion. Adjusted measures of association.
Dividing to conquer.
15. Choosing the statistical test.
Pairing.
16. Comparison of two means. Student’s t test.
The same old story.
17. Analysis of variance of one factor. ANOVA.
More than two is a crowd.
18. Bonferroni’s correction.
When the zeroes of p are important.
19. Lineal correlation. Pearson’s lineal correlation coefficient. Covariance. Independence.
An open relationship.
20. Missing data. Imputation.
…there are not all those who are.
21. Management of non-normal data.
Everything is not normal.
22. Non-parametric tests. Sign test.
At a rough guess.
23. Survival analysis. Survival tables. Censored data.
Censorship.
24. Simple regression. Lineal regression. Logistic regression. Cox regression.
A simple relationship.
25. The least squares method.
The shortest distance.
26. Collinearity. Multiple lineal regression.
Three feet of a cat.
27. Discontinuity regression. Multiple lineal regression.
In search of causality.
28. Resampling techniques. Bootstrapping.
29. Propensity score.
You can’t make a silk purse…
30. Meta-analysis (I). Heterogeneity. Metaregression.
Apples and pears.
31. Meta-analysis (II). Publication bias. Forest plot.
Achilles and Effects Forest.
32. Vote counting in systematic reviews.
The failure of democracy.
33. Effect size based in differences between means.
I am Spartacus.
34. Multivariate analysis techniques.
Exoteric or esoretric?
35. Incorrect uses of statistics.
The cheaters detector.