Answer:
If we compare the p value and the significance level assumed we see that
so we can conclude that we have enough evidence to reject the null hypothesis.
We can say that at 5% of significance the true mean is significantly different from 50.
Step-by-step explanation:
Data given and notation
53, 57, 61, 49, 52, 56, 58, 62, 51, 56
We can calculate the sample mean and deviation with the following formulas:
And we got:
represent the sample mean
represent the sample standard deviation
sample size
represent the significance level for the hypothesis test (ASSUMED).
t would represent the statistic (variable of interest)
represent the p value for the test (variable of interest)
State the null and alternative hypotheses.
We need to conduct a hypothesis in order to check if the true mean is equal or not to 50 :
Null hypothesis:
Alternative hypothesis:
Since we don't know the population deviation, is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:
(1)
t-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".
Calculate the statistic
We can replace in formula (1) the info given like this:
P-value
We need to calculate the degrees of freedom first given by:
Since is a two-sided tailed test the p value would given by:
Conclusion
If we compare the p value and the significance level assumed we see that
so we can conclude that we have enough evidence to reject the null hypothesis.
We can say that at 5% of significance the true mean is significantly different from 50.
