## I. Basic concepts.

- Hypothesis testing (I). Null hypothesis. Alternative hypothesis.

It all spins around the null hypothesis. - Hypothesis testing (II). Alpha. Beta. Power. Sample size.

Size and power. - Hypothesis testing. Fragility index.

The fragility of the EmPress. - Confidence intervals.

Always look for an interval, but of confidence. - Confidence intervals. Prediction intervals. Tolerance intervals.

The poor relations. - Hypothesis contrast. Statistical significance. Tipo 1 error. Tipo 2 error.

To p or not to p…is that the question? - Statistical significance. Clinical relevance.

Even non-significant ps have a little soul. - The meaning of p-value.

Worshipped, but misunderstood. - Fragility, stability and clinical relevance.

Take off your blinders. - Confidence intervals. Statistical significance. Clinical relevance.

Life is not rosy. - Inverse fallacy. Conditionals’ transposition fallacy.

The fallacy of small p. - Sample size.

Size does matter. - Sample size in surveys.

With very little we fine-tune a lot. - Sample size and precision.

Having a large n, who needs a samll p?. - Factor that condition the sample size.

A strenuous effort, though unnecessary. - Sample size for estimating a proportion.

If you doubt, better in the middle. - Sample size for estimating a mean.

A measure with a pedigree. - Sample size for the comparison of two means.

A pair of means. - Sample size calculation in survival studies.

By your actions they will judge you. - Study of qualitative variable dispersion.

Like a forgotten clock. - Location parameters. Arithmetic mean. Median. Mode. Geometric mean. Harmonic mean.

Virtue is the happy medium between two extremes, but… - Choosing between mean and median.

Meat or fish? - Robust location parameters.

A very robust family. - Harmonic mean.

A trickly riddle. - Geometric mean.

Counting bugs, - Robust scale parameters.

Do not be misled by the outliers. - Biased and unbiased estimators.

Why spare one? - Dispersion parameters. Variance. Standard deviation.

Not all deviations are perverse. - Dispersion parameters. Minimum. Maximum. Quartile. Decile. Percentile. Interquartile range.

The most wished statistical for a mother. - Standardization. z score. Frequiency distribution. Probability density curves. Histograms.

Give me a bar and I’ll move the earth. - Simmetry measures. Mean deviation. Distribution bias. Kurtosis.

Playing with powers. - Degrees of freedom. Sample size. Power.

Freedom in degrees. - Regression to the mean.

The curse. - Independency of variables. Paired data.

Independence matters. - Transforming data. Logarithm transformation. Inverse transformation.

Cheating Gauss. - Graphic representation of qualitative variable. Pie chart. Bar chart. Pareto’s graph.

Columns, sectors, and an illustrious italian. - Histogram. Bar graph.

As and egg to a chestnut. - Box plot.

A box with whiskers. - Quantile graph. q-q plot.

Some comparisons are not odious. - The pie chart.

Do not eat too many pies. - Outliers.

Black sheep. - Frequentist vs Bayesian’s statistics.

Rioja vs Ribera?

## II. Probability.

- Normal distribution. Standardization.

The most famous of bells. - Probability distributions. Stdent’s t distribution. Chi-squared distribution. Snédecor’s F distribution.

The big family. - Student`s t distribution.

The brewer. - Discrete probability distributions.

The sleepless naturalist. - Binomial probability (I). Binomial distribution. Bernouilli’s events.

The cooker and his cake. - Binomial probability (II). Survey errors. Lie. Obfuscation factor.

Lie to me. - Binomial probability (III). Calculating number of successes.

Do not gamble. - Binomial probability (IV). The law of small numbers.

The oodities of small towns. - Yates’ continuity correction.

Horror vacui. - Conditioned probability. Bayes’ rule.

The stigma of guilt. - Conditioned probability. Bayes’ theorem.

A case of misleadind probability. - Conditioned probability (II). Bayes’ rule.

Another about coins. - Combinatory and probability.

The deception of intuition. - Probability value when numerator equals zero.

When nothing bad happens, is everything okay?

## III. Statistic tests.

- Hypothesis testing. One-tailed test. Two-tailed test.

The tails of p. - Statistical significance. Power. Type I error. Type II error.

The false coin. - Multiple comparisons. Type I error.

Even a blind donkey… - Statistical test by variable type.

Brown or blond, all bald. - Contingency tables. Residuals. Pearson’s residuals. Standardized residuals. Adjusted residuals. Chi-square.

Residuals management. - Chi-square test (I). Goodness of fit.

Solomonic decisions. - Chi-square test (II). Homogeneity test.

Counting sheep. - Chi-square test (III). Dependent variables.

Studying or working?. - Chi-squared-test (IV). Calulating expected values.

The why and wherefores. - Comparison of two proportions. Chki-square.

All road lead to Rome. - Fishjer’s exact test.

A history of tea and numbers. - Confidence interval for a mean.

The error of confidence. - Concordance. kappa’s interobserver concordance coeficient.

A good agreement?. - Concordance. Bland-Altaman’s method.

Another stone with which nor to trip over. - Stratification. Mantel-Haenszel’s test. Confusion. Adjusted measures of association.

Dividing to conquer. - Choosing the statistical test.

Pairing. - Normality análisys. Contrast and graphical methods.

A picture is worth a thousand words. - Normality analysis. Analytic methods.

Moments. - Comparison of two means. Student’s t test.

The same old story. - Analysis of variance of one factor. ANOVA.

More than two is a crowd. - Bonferroni’s correction.

When the zeroes of p are important. - False discovery rate. False positive. Type 1 error.

Percy Fawcett and the Lost City. - Linear correlation. Pearson’s lineal correlation coefficient. Covariance. Independence.

An open relationship. - Missing data. Imputation.

…there are not all those who are. - Management of non-normal data.

Everything is not normal. - Non-parametric tests. Sign test.

At a rough guess. - Survival analysis. Survival tables. Censored data.

Censorship. - Correlation. Correlation’s coeficients.

Its bark is worst than its bite. - Simple regression. Lineal regression. Logistic regression. Cox regression.

A simple relationship. - The least squares method.

The shortest distance. - Simple lineal regression. Model diagnostics.

Don’t leave things half done. - Collinearity. Multiple lineal regression.

Three feet of a cat. - Quality of a linear regression model.

The mistery of the different colored socks. - Regularization regression techniques. Ridge regression. Lasso regression. Elastics net regression.

An epic dance. - Discontinuity regression. Multiple lineal regression.

In search of causality. - Analysis of logistic regression models.

The paradox of the air. - Resampling techniques. Bootstrapping.

An impossible task. - Propensity score.

You can’t make a silk purse… - Meta-analysis (I). Heterogeneity. Metaregression.

Apples and pears. - Meta-analysis (II). Heterogeneity. Cochran’s Q. I
^{2}. H^{2}.

The polisemy of Q. - Meta-analysis (III). Publication bias. Forest plot.

Achilles and Effects Forest. - Fisher’s z. Meta-analysis with small sample sizes.

The Palace of Probabilities. - Vote counting in systematic reviews.

The failure of democracy. - Effect size based in differences between means.

I am Spartacus. - Multivariate analysis techniques.

Exoteric or esoretric? - Incorrect uses of statistics.

The cheaters detector. - Artificial intelligence. Machine learning.

Hasta la vista, baby. - Artificial neural networks.

Science behind the magic. - Decision trees.

The tree and the labyrinth.