Suppose you are conducting a left tail test and you find a test statistic of 2.445. Suppose that you are conducting the test at the .05 level of significance and there were 42 observations with an unknown population standard deviation. Do you reject or fail to reject the null hypothesis?

If we compare the p value and the significance level given [tex]\alpha=0.05[/tex] we see that [tex]p_v\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis

Step-by-step explanation:

Data given and notation

[tex]\bar X[/tex] represent the sample mean

[tex]s[/tex] represent the sample standard deviation for the sample

[tex]n=42[/tex] sample size

[tex]\mu_o [/tex] represent the value that we want to test

[tex]\alpha=0.05[/tex] represent the significance level for the hypothesis test.

t would represent the statistic (variable of interest)

[tex]p_v[/tex] 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 mean is lower than specified value, the system of hypothesis would be:

Null hypothesis:[tex]\mu \geq \mu_o[/tex]

Alternative hypothesis:[tex]\mu < \mu_o[/tex]

If we analyze the size for the sample is > 30 but we don't know the population deviation so is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:

[tex]t=\frac{\bar X-\mu_o}{\frac{s}{\sqrt{n}}}[/tex] (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

The statistic for this case is given:

[tex]t_{calculated}=2.445[/tex]

P-value

The first step is calculate the degrees of freedom, on this case:

[tex]df=n-1=42-1=41[/tex]

Since is a one side test the p value would be:

[tex]p_v =P(t_{(41)}>2.445)=0.0094[/tex]

Conclusion

If we compare the p value and the significance level given [tex]\alpha=0.05[/tex] we see that [tex]p_v\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis