Yes. The normal distribution is used to approximate a binomial distribution when the sample size (n) times the probability of success (p), and the probability of failure (q) are both greater than or equal to 5. The mean of the normal approximation is n*p and the standard deviation is the square root of n*p*q.
Chat with our AI personalities
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
Discrete data are observations on a variable that which take values from a discrete set.
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.