33 Rare Events, the Sample, Decision and Conclusion

Rare Events

Suppose you make an assumption about a property of the population (this assumption is the null hypothesis). Then you gather sample data randomly. If the sample has properties that would be very unlikely to occur if the assumption is true, then you would conclude that your assumption about the population is probably incorrect. (Remember that your assumption is just an assumption—it is not a fact and it may or may not be true. But your sample data are real and the data are showing you a fact that seems to contradict your assumption.)

For example, Didi and Ali are at a birthday party of a very wealthy friend. They hurry to be first in line to grab a prize from a tall basket that they cannot see inside because they will be blindfolded. There are 200 plastic bubbles in the basket and Didi and Ali have been told that there is only one with a \$100 bill. Didi is the first person to reach into the basket and pull out a bubble. Her bubble contains a \$100 bill. The probability of this happening is $\frac{1}{200}=0.005$. Because this is so unlikely, Ali is hoping that what the two of them were told is wrong and there are more \$100 bills in the basket. A “rare event” has occurred (Didi getting the \$100 bill) so Ali doubts the assumption about only one \$100 bill being in the basket.

Using the Sample to Test the Null Hypothesis

Use the sample data to calculate the actual probability of getting the test result, called the p-value. The p-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.

A large p-value calculated from the data indicates that we should not reject the null hypothesis. The smaller the p-value, the more unlikely the outcome, and the stronger the evidence is against the null hypothesis. We would reject the null hypothesis if the evidence is strongly against it.

Draw a graph that shows the p-value. The hypothesis test is easier to perform if you use a graph because you see the problem more clearly.

Example 8.5

Suppose a baker claims that his bread height is more than 15 cm, on average. Several of his customers do not believe him. To persuade his customers that he is right, the baker decides to do a hypothesis test. He bakes 10 loaves of bread. The mean height of the sample loaves is 17 cm. The baker knows from baking hundreds of loaves of bread that the standard deviation for the height is 0.5 cm. and the distribution of heights is normal.

Note

In this example, we are testing a claim about means. Since the baker knows the population standard deviation, we will be using the normal distribution. This is unusual however; if we are testing a claim about a population’s mean, that suggests we don’t know what the population mean is, and if we don’t know what the population mean is, we probably don’t know what the population standard deviation is. So generally if we are running a hypothesis test about a population mean, we will be using the t-distribution instead.

Claim: μ > 15	H1: μ > 15
Opposite: μ ≤ 15	H0: μ = 15

The null hypothesis could be H₀: μ = 15 The alternate hypothesis is H₁: μ > 15

The words “is more than” translates as a “>” so “μ > 15″ goes into the alternate hypothesis. The null hypothesis must always contains the equal symbol “=”.

Since σ is known (σ = 0.5 cm.), the distribution for the population is known to be normal with mean μ = 15 and standard deviation $\frac{\sigma}{\sqrt{n}}=\frac{0.5}{\sqrt{10}}=0.16$

Suppose the null hypothesis is true (the mean height of the loaves is  15 cm). Then is the mean height (17 cm) calculated from the sample unexpectedly large? The hypothesis test works by asking the question how unlikely the sample mean would be if the null hypothesis were true. The graph shows how far out the sample mean is on the normal curve. The p-value is the probability that, if we were to take other samples, any other sample mean would fall at least as far out as 17 cm.

The p-value, then, is the probability that a sample mean is the same or greater than 17 cm. when the population mean is, in fact, 15 cm. We can calculate this probability using the normal distribution for means.

Figure 9.2

p-value= $P(\bar x > 17)$ which is approximately zero.

A p-value of approximately zero tells us that it is highly unlikely that a loaf of bread rises no more than 15 cm, on average. That is, almost 0% of all loaves of bread would be at least as high as 17 cm. purely by CHANCE had the population mean height really been 15 cm. Because the outcome of 17 cm. is so unlikely (meaning it is happening NOT by chance alone), we conclude that the evidence is strongly against the null hypothesis (the mean height is 15 cm.). There is sufficient evidence that the true mean height for the population of the baker’s loaves of bread is greater than 15 cm.

Decision and Conclusion

A systematic way to make a decision of whether to reject or not reject the null hypothesis
is to compare the p-value and a preset or preconceived α (also called a “significance level”). A preset α is the probability of a Type I error (rejecting the null hypothesis when the null hypothesis is true). It may or may not be given to you at the beginning of the problem.

When you make a decision to reject or not reject H₀, do as follows:

• If α > p-value, reject H₀. The results of the sample data are significant. There is sufficient evidence to conclude that H₀ is an incorrect belief and that the alternative hypothesis, H₁, may be correct.
• If α ≤ p-value, do not reject H₀. The results of the sample data are not significant.There is not sufficient evidence to conclude that the alternative hypothesis,H₁, may be correct.
• When you “do not reject H₀“, it does not mean that you should believe that H₀ is true. It simply means that the sample data have failed to provide sufficient evidence to cast serious doubt about the truthfulness of H_o.

Conclusion: After you make your decision, write a thoughtful conclusion about the hypotheses in terms of the given problem.