Données : types et distribution

Parametric data assumes a specific distribution pattern and includes numerical observations like weight or tablet counts. Nonparametric data does not follow any specific distribution and comprises categorical observations such as gender or pain scale ratings. Understanding parametric versus nonparametric data is essential for selecting appropriate statistical analysis methods.

Continuous data represents measurements that can take any value within a range, such as patient weight or drug concentration levels. Discrete data consists of countable whole numbers, like the number of tablets administered or patient count. Both are parametric data types used in biostatistical analysis.

Nominal data categorizes observations without any inherent order, such as gender or drug type classifications. Ordinal data has a meaningful sequence or ranking, like pain scale ratings from mild to severe. Both are nonparametric categorical data types commonly used in biostatistics.

Normal or Gaussian distribution is a symmetrical arrangement where mean, median, and mode coincide. Baby birth weights exemplify this pattern. Normal distribution is fundamental in biostatistics because many parametric statistical tests assume data follows this distribution pattern for valid analysis.

A positive skew shows data asymmetrically distributed with a long tail extending toward the right. Drug metabolite concentrations in patient blood exemplify positive skew, where most values cluster lower but some extremely high values pull the tail rightward, creating asymmetry.

Negative skew has a long tail extending toward the left, indicating most data points cluster higher with some extremely low values. The cost of branded drugs demonstrates negative skew. Both skewed distributions represent asymmetrical data arrangements unlike the symmetrical normal distribution.

Data distribution determines which statistical methods to analyze parametric data are appropriate for analysis. Parametric tests require normally distributed data, while nonparametric tests suit skewed or non-normal distributions. Recognizing whether data follows normal, positive, or negative skew patterns ensures accurate interpretation.