The uniform and the normal distributions have only one feature in common; they are both continuous distributions with two parameters. A continues distribution, in this case, means that probabilities are described based on the possible values of a continuous random variable. There are several differences between the normal and the uniform distribution. The normal distribution has infinite support while the uniform distribution has finite support. The normal distributions usually arise from the central limit theorem, a case which does not happen in the uniform distribution. Both of the two distributions are both symmetric; this means that all the medians are equal and the values higher than the range equally correspond lower than the mean.
The only feature that is common between the uniform and the normal distributions is that the profanities in them can be described based on the possible values of continuous random values. They both exhibit a feature of constant distribution. The differences that exist between these two distributions are that in a uniform distribution, all the values are likely within a range and it is almost impossible for the values to go beyond the range. In the normal distribution, the values cluster around the mean, and it has every allowable value equally likely. The normal distributions were coined from the central limit theorem while the uniform distributions are not borrowed from any theory. In both the distributions, the medians are equal, and the values higher than the range cannot go beyond the mean.