In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).

Distributions in biostatistics represent how data points are arranged on a graph. The most common is the normal or Gaussian distribution, where the mean, median, and mode align, as seen with baby birth weights. However, some data exhibit skewed distributions, with data points asymmetrically distributed around the mean. A positive skew has a long tail towards the right, like drug metabolite concentrations with few very high values. Conversely, a negative skew has a tail towards the left, such as the curve of students' scores on an easy exam. Understanding these data types and distributions is essential for accurate biostatistical analysis and interpretation.