Below are a series of observed values for a single-sample z test. For each, decide whether one can reject or fail to reject the null hypothesis. (Hint: Find critical value of z and plot both z and the critical value on a normal distribution to determine whether z falls in the critical zone) a)z = 1.89, α = .05, two-tailed,b)z = 1.90, α = .05, one-tailed (assume rare zone is on the right side of the distribution,c)z = -2.56, α = .01, two-tailed ,
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!
Below are a series of observed values for a single-sample z test. For each, decide whether one can reject or fail to reject the null hypothesis. (Hint: Find critical value of z and plot both z and the critical value on a
a)z = 1.89, α = .05, two-tailed,b)z = 1.90, α = .05, one-tailed (assume rare zone is on the right side of the distribution,c)z = -2.56, α = .01, two-tailed ,
d)z = -2.01, α = .01, one-tailed (assume rare zone is on the left side of the distribution)
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