Final answer:
Continuous probability distributions, such as the uniform distribution, have a total area under their curve equal to 1, representing certainty of an outcome within the distribution's range. Individual probabilities are determined by the area for a specific interval, and the empirical rule applicable to normal distribution can't be used for uniform distribution.
Step-by-step explanation:
The question focuses on properties of continuous probability distributions and the application of certain probability rules. The empirical rule, also known as the 68-95-99.7 rule, refers to the normal distribution and cannot be directly applied to the uniform distribution, which has a different shape and characteristics. The uniform distribution is a type of continuous distribution that gives equal probability to all outcomes over a certain range and is represented by a rectangle in graphical form.
All continuous probability distributions, including the uniform distribution, share the property that the total area under their probability density function (pdf) curve is equal to 1. This area represents the entire range of possible outcomes or the certainty that some outcome within the distribution's bounds will occur. Individual probabilities within these distributions are found by calculating the area under the pdf curve for a specific interval of values, which necessitates the use of geometric, formulaic, or computational methods to determine probabilities for intervals rather than discrete values.