The average body temperature for healthy adults is 98.6 °F. Is this statement tr

Question

The average body temperature for healthy adults is 98.6 °F. Is this statement true? Do all healthy people have exactly the same body temperature? A study was conducted a few years go to examine this belief.
The body temperatures of n = 130 healthy adults were measured (half male and half female). The average temperature from the sample was found to be x = 98.249 with standard deviation s = 0.7332.

Do these statistics contradict the belief that the average body temperature is 98.6? If the true average temperature is indeed 98.6 °F and we obtain a sample of n = 130 healthy adults, we would not expect the sample mean to come out exactly equal to 98.6 °F. We observed x = 98.249- can this deviation from 98.6 be explained by chance or is it unlikely we would observe a value this different from 98.6?

Two people debating this issue could come to different conclusions.

Using the methods introduced in this module, discuss how you would determine if the data contradicts the hypothesis that the average body temperature is 98.6°F.

Compare and discuss your methods with your classmates. Also respond too a classmate.

Classmate response:
The sample of body temperatures of n= 130 of healthy men and women is just that, a sample of the population. There is always that chance for a variation in sampling. This being said, by watching the sample mean /x = 98.249 will not cancel out the chances that the population mean is 98.6*F. In order to follow the proper procedure for using hypothesis test to support the claims, even with such a high number is a one sample test for mean, which a Z test would be the appropriate means.
The null and alternative hypothesis would be;
H0:µ = 98.6 and H1:µ 98.6
The test statistic would show = 98.249 -98.6/0.7332/130=5.458. The critical values of 5% significance levels are +/1.96. This will show that the test statistics are falling in the denial area, which we are denying the null hypothesis at 5 % significance level in the conclusion observed in this test supporting enough evidence that the population mean body temperature significantly differs from 98.6*F at 5% significance levels.
Even though the evidence shows favorable for the population having a temperature of 98.6 *F, I personally feel that not everyone runs normal temperatures, especially myself. I have always run below normal, and if I am 98.6 then I am sick.

The average body temperature for healthy adults is 98.6 °F. Is this statement true? Do all healthy people have exactly the same body temperature? A study was conducted a few years go to examine this belief.
The body temperatures of n = 130 healthy adults were measured (half male and half female). The average temperature from the sample was found to be x = 98.249 with standard deviation s = 0.7332.

Do these statistics contradict the belief that the average body temperature is 98.6? If the true average temperature is indeed 98.6 °F and we obtain a sample of n = 130 healthy adults, we would not expect the sample mean to come out exactly equal to 98.6 °F. We observed x = 98.249- can this deviation from 98.6 be explained by chance or is it unlikely we would observe a value this different from 98.6?

Two people debating this issue could come to different conclusions.

Using the methods introduced in this module, discuss how you would determine if the data contradicts the hypothesis that the average body temperature is 98.6°F.

Compare and discuss your methods with your classmates. Also respond too a classmate.

Classmate response:
The sample of body temperatures of n= 130 of healthy men and women is just that, a sample of the population. There is always that chance for a variation in sampling. This being said, by watching the sample mean /x = 98.249 will not cancel out the chances that the population mean is 98.6*F. In order to follow the proper procedure for using hypothesis test to support the claims, even with such a high number is a one sample test for mean, which a Z test would be the appropriate means.
The null and alternative hypothesis would be;
H0:µ = 98.6 and H1:µ 98.6
The test statistic would show = 98.249 -98.6/0.7332/130=5.458. The critical values of 5% significance levels are +/1.96. This will show that the test statistics are falling in the denial area, which we are denying the null hypothesis at 5 % significance level in the conclusion observed in this test supporting enough evidence that the population mean body temperature significantly differs from 98.6*F at 5% significance levels.
Even though the evidence shows favorable for the population having a temperature of 98.6 *F, I personally feel that not everyone runs normal temperatures, especially myself. I have always run below normal, and if I am 98.6 then I am sick.


The body temperatures of n = 130 healthy adults were measured (half male and half female). The average temperature from the sample was found to be x = 98.249 with standard deviation s = 0.7332.

Do these statistics contradict the belief that the average body temperature is 98.6? If the true average temperature is indeed 98.6 °F and we obtain a sample of n = 130 healthy adults, we would not expect the sample mean to come out exactly equal to 98.6 °F. We observed x = 98.249- can this deviation from 98.6 be explained by chance or is it unlikely we would observe a value this different from 98.6?

Two people debating this issue could come to different conclusions.

Using the methods introduced in this module, discuss how you would determine if the data contradicts the hypothesis that the average body temperature is 98.6°F.

Compare and discuss your methods with your classmates. Also respond too a classmate.

Classmate response:
The sample of body temperatures of n= 130 of healthy men and women is just that, a sample of the population. There is always that chance for a variation in sampling. This being said, by watching the sample mean /x = 98.249 will not cancel out the chances that the population mean is 98.6*F. In order to follow the proper procedure for using hypothesis test to support the claims, even with such a high number is a one sample test for mean, which a Z test would be the appropriate means.
The null and alternative hypothesis would be;
H0:µ = 98.6 and H1:µ 98.6
The test statistic would show = 98.249 -98.6/0.7332/130=5.458. The critical values of 5% significance levels are +/1.96. This will show that the test statistics are falling in the denial area, which we are denying the null hypothesis at 5 % significance level in the conclusion observed in this test supporting enough evidence that the population mean body temperature significantly differs from 98.6*F at 5% significance levels.
Even though the evidence shows favorable for the population having a temperature of 98.6 *F, I personally feel that not everyone runs normal temperatures, especially myself. I have always run below normal, and if I am 98.6 then I am sick. The sample of body temperatures of n= 130 of healthy men and women is just that, a sample of the population. There is always that chance for a variation in sampling. This being said, by watching the sample mean /x = 98.249 will not cancel out the chances that the population mean is 98.6*F. In order to follow the proper procedure for using hypothesis test to support the claims, even with such a high number is a one sample test for mean, which a Z test would be the appropriate means.
The null and alternative hypothesis would be;
H0:µ = 98.6 and H1:µ 98.6
The test statistic would show = 98.249 -98.6/0.7332/130=5.458. The critical values of 5% significance levels are +/1.96. This will show that the test statistics are falling in the denial area, which we are denying the null hypothesis at 5 % significance level in the conclusion observed in this test supporting enough evidence that the population mean body temperature significantly differs from 98.6*F at 5% significance levels.
Even though the evidence shows favorable for the population having a temperature of 98.6 *F, I personally feel that not everyone runs normal temperatures, especially myself. I have always run below normal, and if I am 98.6 then I am sick.

Explanation / Answer

TO your arguement:

This sample lies in the denial region and therefore, chances of getting such sample ( whose mean temp is NOT 98.6) is unusual. To conclude this sample is not from the population. But if we were to accept this sample, this sample is negating the fact that mean temperature is 98.6 and concluding the fact that population mean temperature is actually not equal to 98.6.

So, when you argue that your temperature is rarely 98.6 , you' right. When you say it' less than 98.6 then that case(your) is covered in the alternate hypothesis of NOT EQUAL TO 98.6

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