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Week 1 Lecture Video for BUS 7106 – Statistics II

In the first week of this course, you will revisit fundamentals of introductory

statistics including topics of hypothesis testing, probability, and a refresher

of how to generate descriptive statistics and correlation analysis using

standard software used in business and graduate school.

Hypothesis testing is a standard research tool used to determine the

statistical significance of research findings. The thinking behind hypothesis

testing is that there is a cutoff, so to speak, of when a statistical finding is

considered to be significant and worthy of further exploration. When a

traditional statistical test (e.g., correlation, t-test, ANOVA, etc.) is

calculated, it reveals a probability value (p-value). This is compared to a

cutoff value to determine this statistical significance. For example, if a cutoff

of 5% is used, then the p-value must be equal to or less than .05 for the

finding to be considered statistically significant (hereafter referred to as

significant). A p-value of greater than this .05 cutoff is considered to be nonsignificant. It is important to note that while a finding is non-significant, it

does not mean that it is not relevant.

The .05 cutoff value, referred to above, is not arbitrary. This cutoff, or alpha,

value is the most commonly used cutoff for significance in research across a

variety of disciplines (e.g., leadership, management, business, the natural

sciences, healthcare, sociology, etc.). The reason this value is common is

that it is the point at which Types I and II error are balanced. If you think

back to your last statistics course, there is always the possibility of

committing an error in hypothesis testing. And to raise or lower the alpha

value will have a resulting impact on Type II value. For example, if you

reduce the alpha cutoff to .01 to demonstrate more stringent evidence of

significance, you increase the risk for the Type II error.

In a review of the types of error, for example, an error may result in a false

positive or false negative. A false positive is thinking that you have a finding,

but in fact it is not real. It is akin to a blood test in which you think you may

have an illness, but the test yielded the false positive. This is known as being

gullible to something that does not exist.

The false negative is not knowing that a phenomenon is in play when it is.

Continuing the previous example, a blood test does not yield evidence that

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illness is in effect, but in fact it is. This is being blind. So the question

becomes, would you rather be gullible or blind?

The preceding discussion is relevant to the first Week 1 assignment. While

hypothesis testing has been a standard research determinant of significance

for many decades, it has its criticisms. Two that are related are noted here.

The first is the alpha cutoff. While the .05 value balances the risk of the two

types of error, what if a statistical test yields p =.06? Does this mean that

since it does not meet the alpha cutoff value of .05, and is thus nonsignificant, that it is not somehow meaningful? In effect, the .05 would

indicate that the possibility of a Type I error is 5%. A result of .06 means

that the possibility of a Type I error is 6%. Is the finding, albeit close, not

worthy of discussion? A finding may be non-significant, but still meaningful.

Conversely, given a large enough sample size, a statistically significant

result may not be meaningfully relevant.

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