Suppose you have a normally distributed set of data pertaining to a standardized test. The mean score is 500 and the standard de
1 answer:
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Answer:
Comparing the p value with a significance level for example we see that
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can't say that the population mean for the athletes is significantly lower than the population mean for non athletes.
Step-by-step explanation:
Data given and notation
represent the mean for athletes
represent the mean for non athletes
represent the sample standard deviation for athletes
represent the sample standard deviation for non athletes
sample size for the group 2
sample size for the group 2
Significance level provided
t would represent the statistic (variable of interest)
Concepts and formulas to use
We need to conduct a hypothesis in order to check if the population mean for athletes is lower than the population mean for non athletes, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
We don't have the population standard deviation's, we can apply a t test to compare means, 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 whether the means of two groups are equal to each other.
With the info given we can replace in formula (1) like this:
P value
We need to find first the degrees of freedom given by:
Since is a one left tailed test the p value would be:
Comparing the p value with a significance level for example we see that
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can't say that the population mean for the athletes is significantly lower than the population mean for non athletes.