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Running head: BUSN311 - Quantitative Methods and Analysis
Unit 4 – Hypothesis Testing & Variance
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American InterContinental University
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BUSN311 - Quantitative Methods and Analysis
Abstract
Intrinsic factors about job satisfaction were analyzed to determine if there was any correlation
between genders and extrinsic factors about job satisfaction were analyzed to determine if there
was any correlation between hourly vs. salaried workers. There was not enough evidence to
conclude the former, but there was for the latter.
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BUSN311 - Quantitative Methods and Analysis
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Introduction
The lesson required the student to use statistics to formulate hypotheses about office job
satisfaction surveys and then test them to see if those same hypotheses should be rejected. The
student was required to use Excel to conduct t tests, calculate test statistics and critical values.
Finally, the student had to interpret the statistical results to make observations for an office
manager.
Hypothesis Test #1 Looking at Intrinsic Satisfaction by Gender
Null and alternate hypotheses.
x1  Intrinsic job satisfaction for men
H 0 : x1  x 2


H a : x1  x 2
x 2  Intrinsic job satisfaction for women
( = .05)
The test
A separate F test was conducted to confirm that the sample variances (S12 and S22) were
equal. From this result, the two-tailed Student’s t test was done with a pooled estimate for the
standard deviation, SP (Johnson, 1976). Using Excel’s TTEST function, the probability that the
populations have the same mean intrinsic job satisfaction was 0.90, or P(T>0.126)=0.90.
The significance level () was given as 0.05. The test statistic t was computed to be
0.126. Using Excel’s TINV function with 26 degrees of freedom and a probability of 0.05, the
critical value for t was found to be 2.056.
State your decision
The analysis indicates that I should fail to reject the null hypothesis statement.
Explanation of decision made
The test statistic (t=0.126) is not in the rejection region (T>2.056), therefore, do not reject
the null hypothesis.
BUSN311 - Quantitative Methods and Analysis
Applications for managers
The manager should feel confident that the office environment is not causing one gender
to enjoy/dislike the job more than the other. The information is valuable so that the manager can
focus their efforts in other areas as this is not a problem area.
Hypothesis Test #2 Looking at Extrinsic Satisfaction by Position
Null and alternate hypotheses
x1  Extrinsic job satisfaction for hourly workers
x 2  Extrinsic job satisfaction for salaried workers
H 0 : x1  x 2


H a : x1  x 2
( = .05)
The test
A separate F test was conducted to confirm that the sample variances (S12 and S22) were
unequal. From this result, the two-tailed Student’s t test was done with both sampled standard
deviations (Johnson, 1976). Using Excel’s TTEST function, the probability that the populations
have the same mean extrinsic job satisfaction was 0.0000218, or P(T< -4.642)=0.0000218.
The significance level () was given as 0.05. The test statistic t was computed to be
-4.642. Using Excel’s TINV function with 22 degrees of freedom and a probability of 0.05, the
critical value for t was found to be -2.074.
State your decision
The analysis indicates that I should reject the null hypothesis statement.
Explanation of decision made
The test statistic (t= -4.642) is in the rejection region (T< -2.074), therefore, reject the
null hypothesis.
Applications for managers
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BUSN311 - Quantitative Methods and Analysis
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The hourly workers clearly have a lower level of extrinsic job satisfaction than their
salaried counterparts. This could be used in determining new ways to motivate employees and
for management to be prepared for labor negations.
Z and T Tests
The Z test is used when the population you are examining is sufficiently large (N>30) and
can assumed to be normally distributed. Alternatively, the T test is used when you do not have
enough samples (N<30), but the distribution is still symmetric about the mean and is generally
bell-shaped (Johnson, 1976).
Samples and Populations
A sample is a subset of an entire population who’s mean and variance are known. When one
wants to make comparisons about a particular sample to its corresponding population, hypothesis
testing can be done. Samples are used so that you can conduct an experiment on a much smaller
portion of the population and then test that sample to see how it compares to the larger
population and determine if the outcome of the experiment is significant. Drug testing is done
this way to compare the effects of a drug on a sample of patients who received it compared to the
larger population who did not receive it.
Conclusion
Hypothesis testing is an excellent way to use sampled data about a population to determine
certain characteristics about the whole or to determine if some experiment you did on the sample
had statistically significance results.
BUSN311 - Quantitative Methods and Analysis
References
Johnson, R. (1976). Elementary statistics (2nd ed.). North Scituate, MA: Duxbury Press.
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