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1. A binomial random variable is an example of a discrete variable
2. Inbinomial experiments, the probability of success is the same on every trial
3. A coin toss experiment represents a binomial experiment only if the coin is balanced, i.e., p = 0.5.
4. As a rule of thumb, if the sample size n is large relative to the population size N in particular, if (n / N) > 0.05 then the resulting experiment will not be binomial.
5. If x is a binomial random variable with n = 20, and p = 0.5, then P(x = 20) = 1.0.
6. The number of defects in a random sample of 200 parts produced by a machine is binomially distributed with p = .03. Based on this information, the standard deviation of the number of defects in the sample is 5.82.
7. The mean and variance of the Poisson distribution are equal
8. The Poisson random variable is the number of successes achieved when a random sample of size n is drawn without replacement from a population of size N within which M units have the characteristic that denotes success.
9. Hypergeometric probability distributions is an example of discrete probability distributions.