Final answer:
The normal distribution is defined by two parameters: the mean (μ) and the standard deviation (σ). It is represented by a bell-shaped curve and is used across many disciplines.
Step-by-step explanation:
The normal distribution is a fundamental concept in statistics that is often represented by a bell-shaped curve. This distribution is completely described by two parameters: the mean (μ) and the standard deviation (σ). When a variable X follows a normal distribution, it is denoted as X-N(μ, σ). The mean determines the center of the distribution, aligning with the highest point on the bell curve, while the standard deviation defines the spread or width of the curve, marking the distance to the curve's inflection points from the center.
The standard normal distribution is a specific case where the mean (μ) is 0 and the standard deviation (σ) is 1, represented by the variable Z. It is used in various fields, indicating its wide applicability across disciplines such as psychology, business, and economics. This distribution is continuous, implying that it allows for an infinite number of possible outcomes within a range.