Standing eye heights of women normally distributed with a mean of 1516 mm and a standard deviation of 63 mm. What % of women have a standing eye height between 1420 mm and 1560 mm, and what is the probability that a group of 20 women has an average standing eye height that is less than 1500 mm? Even though the sample size is less than 30, why can the Central Limit Theorem still be applied?
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!
Standing eye heights of women
What % of women have a standing eye height between 1420 mm and 1560 mm, and what is the probability that a group of 20 women has an average standing eye height that is less than 1500 mm? Even though the
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