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The term that accurately reflects the expected distribution of outcomes in a large population is called a normal distribution. A normal distribution is a common probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. This creates a bell-shaped curve when graphed.

In a large population, many variables, such as heights, IQ scores, or measurement errors, tend to follow this distribution due to the central limit theorem. The central limit theorem states that the means of samples drawn from a population with any shape distribution will tend to be normally distributed if the sample sizes are sufficiently large. This characteristic makes the normal distribution a fundamental concept in statistics, as it allows for the use of various statistical techniques and inference methods that assume data is normally distributed.

Other distributions mentioned have their specific contexts. A skewed distribution indicates that the data tail is longer on one side; a bi-modal distribution has two distinct peaks, suggesting two prevalent outcomes within the data; and a uniform distribution implies that all outcomes are equally likely. Each of these could apply under different circumstances but does not represent the typical scenario seen in large populations, where the normal distribution prevails.