**Core Concept**
Continuous distributions in statistics refer to probability distributions where the variable can take any value within a given range, often represented on a continuous scale. This contrasts with discrete distributions, where variables can only take certain distinct values.

**Why the Correct Answer is Right**
A normal distribution, also known as a Gaussian distribution, is a classic example of a continuous distribution. It is characterized by its bell-shaped curve, with the majority of data points clustered around the mean and tapering off gradually towards the extremes. The normal distribution is often represented by the equation f(x) = (1/√(2πσ^2)) * e^(-(x-μ)^2/(2σ^2)), where μ is the mean and σ is the standard deviation. This distribution is ubiquitous in statistics and is used to model a wide range of phenomena, from the heights of humans to the stock prices of companies.

**Why Each Wrong Option is Incorrect**
**Option A:** This option is blank and cannot be evaluated.
**Option B:** Discrete distributions, such as the Poisson distribution, are characterized by their ability to take only specific, distinct values.
**Option C:** This option is blank and cannot be evaluated.

**Clinical Pearl / High-Yield Fact**
The normal distribution is often used as a model for real-world data, but it's essential to remember that not all data follows a normal distribution. The choice of distribution should be based on the characteristics of the data, and using the wrong distribution can lead to inaccurate conclusions.

**Correct Answer: C. Normal distribution**