Beyond representative sampling, what assumptions are needed to satisfy the requirements of a one-sample means interval? The sample size should be at least 30 and the population have a normal distribution The sample size should be at least 30 or the population have a normal distribution The number of successes and failures should both be at least 5 and the number of successes should follow a binomial distribution The number of successes and failures should both be at least 5 or the number of successes should follow a binomial distribution
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Beyond representative sampling, what assumptions are needed to satisfy the requirements of a one-sample means interval?
- The
sample size should be at least 30 and the population have anormal distribution - The sample size should be at least 30 or the population have a normal distribution
- The number of successes and failures should both be at least 5 and the number of successes should follow a binomial distribution
- The number of successes and failures should both be at least 5 or the number of successes should follow a binomial distribution
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