When noticed that a normally distributed variable you collected data on showed a floor effect, you would surmise that it is scores with a larger range of possible scores.
All kinds of variables in the natural and social sciences are normally or nearly normally distributed. Height, birth weight, reading ability, job satisfaction, and SAT score are just a few examples of such variables.
You can also visually check for normality by plotting the frequency distribution (also called a histogram) of your data and visually comparing it to the normal distribution (overlaid in red). A frequency distribution places each data point in a separate bin. For example, (-10,-5], (-5, 0], (0, 5], etc.
Just prediction error. Prediction error is the deviation of the model prediction result from the actual result. The prediction error should follow a normal distribution with a mean of 0.
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