Statistics

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The mystery of the imperfect crime

The tau-squared represents the variability of effects between the different populations from which the primary studies of a systematic review are derived, according to the assumption of the random effects model of meta-analysis. Its usefulness for weighting studies and for calculating prediction intervals is described, understanding how its significance goes beyond being a mere indicator of heterogeneity.

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Between preferences and coincidences

Cramer's V allows the strength of the association between two categorical (nominal) variables, not ordinal, to be quantified. It is especially useful when the variables have multiple categories, since it allows the strength of the association to be condensed into a single figure. Its values range from 0, no association, to 1, a perfect association.

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The three musketeers

There are three important components involved in the training process of a machine learning algorithm: the loss function, the performance metric, and the validation control. The need to balance accuracy and predictive capacity to obtain robust and effective models is emphasized.

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Too many paths, no final destination

Contrary to what it could be supposed, the inclusion of a large number of variables in a linear regression model can be counterproductive to its performance, producing overfitting of the data and decreasing the capacity for generalization. This is known as the curse of multidimensionality.

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The megapixel trap

Visual manipulation of data using poorly designed charts can distort data interpretation. The most common errors, such as missing axes, manipulated scales, and confusing pie charts, are described, which can lead to erroneous conclusions. Learning to detect these errors will allow us to improve our ability to visually analyze and interpret data.

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