Which type of distribution results from continuous data?

The normal distribution is the correct choice for this question as it specifically pertains to continuous data. Continuous data refers to numerical values that can be divided infinitely into smaller increments. The normal distribution, characterized by its bell-shaped curve, models many naturally occurring phenomena and is defined by its mean and standard deviation.

One of the key features of the normal distribution is that it can represent a wide variety of real-world scenarios where observations are symmetrically distributed around the mean. It is particularly prominent in quality control, measurement errors, and many physical measurements that tend to cluster around an average value.

In contrast, the other distributions mentioned have different applications and characteristics. The binomial distribution deals with discrete data, specifically outcomes of a fixed number of trials with two possible outcomes (e.g., success and failure). The Poisson distribution, also related to discrete data, models the number of events occurring in a fixed interval of time or space. The Weibull distribution is used primarily in reliability analysis and survival studies, often dealing with life data which can be continuous but is not primarily defined as such in standard contexts.

Thus, the association of the normal distribution with continuous data makes it the most appropriate answer to the question.