A researcher is interested in infants’ attention to their mothers’ voices. The research wants to know if the infant’s attention increases to the mother’s voice after spending the first week of life with the mother. The researcher selects 10 full-term infants with normal health who experienced uncomplicated deliveries and tested the number of seconds that the infant oriented in the direction of the mother’s voice on Day 1 and on Day 7 after delivery. These fictional data appear below. Use an α = 0.01 Day 1 Day 7 6 7 5 7 3 6 8 8 7 9 6 8 5 5 6 8 8 7 5 8
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!
A researcher is interested in infants’ attention to their mothers’ voices. The research wants to know if the infant’s attention increases to the mother’s voice after spending the first week of life with the mother. The researcher selects 10 full-term infants with normal health who experienced uncomplicated deliveries and tested the number of seconds that the infant oriented in the direction of the mother’s voice on Day 1 and on Day 7 after delivery. These fictional data appear below. Use an α = 0.01
Day 1 Day 7
6 7
5 7
3 6
8 8
7 9
6 8
5 5
6 8
8 7
5 8
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