**Question:** All of the following is an example of a nominal scale except -

A. Age (measured in numbers)
B. Blood Pressure (measured in mmHg)
C. Temperature (measured in Celsius or Fahrenheit)
D. Gender (male or female)

**Correct Answer:** .

**Core Concept:**
In statistics, a nominal scale is a type of measurement that categorizes data into groups or classes based on their characteristics, rather than their numerical values. Nominal scales do not have any inherent order or relationship between the categories, but they can provide information about the presence or absence of certain attributes or characteristics.

**Why the Correct Answer is Right:**
Option B (Blood Pressure) is incorrect because it is measured in mmHg (millimeters of mercury), which represents a numerical value and thus involves a unit of measurement. Blood pressure is considered a quantitative scale, as it involves numerical data representing force.

**Why Each Wrong Option is Incorrect:**
A. Age (measured in numbers) is incorrect because it represents numerical data. Although age is categorized into years or decades, it still has a numerical value and is thus considered a quantitative scale.

C. Temperature (measured in Celsius or Fahrenheit) is incorrect because it represents numerical data. Temperature is measured using a scale (Celsius or Fahrenheit) but it still involves numerical values and is therefore considered a quantitative scale.

D. Gender (male or female) is incorrect because it is a categorical variable with no numerical values. Gender is categorized into two classes (male or female) without any numerical measurements, making it an example of a nominal scale.

**Clinical Pearl:**
Understanding the different types of measurement scales, including nominal, ordinal, interval, and ratio scales, is essential for interpreting and analyzing medical data appropriately. Nominal scales provide categorical information without numerical values, while quantitative scales involve numerical values, allowing for more detailed analysis and comparison of data.