Which variable is most likely to follow a normal model?

The variable that is most likely to follow a normal model is head circumference. This type of measurement, like many biological characteristics, often exhibits a bell-shaped distribution in a population when a large enough sample is considered. The normal distribution is characterized by being symmetric around a mean, with most of the values clustering near that mean and fewer values appearing as they move away from it.

Although body temperature and height of individuals can also approximate a normal distribution, they may be influenced by factors such as environmental changes or age, which could affect the shape of their distributions. Randomly selected test scores can sometimes show a normal distribution, but they can also be skewed or contain outliers due to factors like socioeconomic status, educational disparities, or the nature of the test itself.

In contrast, head circumference tends to be a relatively stable measurement that is less likely to be affected by outside variables, making it a prime candidate for following a normal distribution pattern. This is especially true when considering a sufficiently large and diverse population.