Temperature is a measurement that can take on any value within a range, including fractions and decimals. Because it is measured on a continuous scale rather than counted in discrete units, it is classified as a continuous variable.

A discrete random variable is defined as a variable that can only assume a countable number of distinct values, typically integers. Unlike continuous variables, which can take any value within a range, discrete variables represent counts or distinct categories, such as the number of students in a class or the outcome of a die roll.

While the amount of money is numerical, the method of payment (cash vs. cheque) is a categorical or qualitative variable. In statistical terms, this is a non-numerical outcome because it represents a classification or attribute rather than a quantitative measurement. This distinction is important for selecting appropriate statistical tests.

Marital status is a qualitative characteristic that cannot be measured numerically. In statistics, such non-numerical characteristics are classified as attributes. Unlike variables, which take on numerical values that can be manipulated mathematically, attributes represent categories or labels that describe the state or quality of an individual or object within a dataset.

The number of road accidents is a discrete variable because it represents a count of distinct events. Discrete variables can only take on specific, countable values, typically integers (e.g., 0, 1, 2, 3 accidents). Unlike continuous variables, which can take any value within a range, discrete variables are characterized by gaps between possible values, making them suitable for counting rather than measuring.

The number of accidents is a count of events, which can only take on non-negative integer values (0, 1, 2, etc.). Because these values are distinct and countable, they are classified as a discrete variable. Unlike continuous variables, which can take any value within an interval, discrete variables are characterized by gaps between possible values, making them suitable for representing counts of occurrences.

Data refers to the raw facts, figures, or observations collected during a research process. These observations are the building blocks of statistical analysis. Once collected, data can be processed, summarized, and analyzed to draw inferences about the population from which the sample was drawn. It is the fundamental input for any statistical investigation or empirical study.

Raw data refers to the original, unprocessed information collected during a study. Because it has not yet been subjected to statistical processing, sorting, or grouping, it remains in its initial, unorganized state, which is why it is referred to as 'raw'.

In statistical notation, it is standard convention to use Roman letters (such as x-bar for the sample mean or s for the sample standard deviation) to denote statistics derived from a sample. Conversely, Greek letters (such as mu or sigma) are reserved for population parameters.

Height is a measurement that can take on any value within a specific range, including fractional or decimal values. Because it is measured on a continuous scale rather than counted in whole units, it is classified as continuous data. This distinguishes it from discrete data, which typically involves counts of distinct items or events.