Chapter 11 Cheat Sheet

University: University of Ontario Institute of Technology

Course: Statistics (BUSI1450U)

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Final Cheat Sheet

Chapter 11 Hypothesis Testing

If the calculated probability is smaller than a certain amount, we say: “we reject the claim”, otherwise,

we say: “we do not have enough evidence to reject the claim”

The null hypothesis, denoted by �0, is a tentative assumption about a population parameter:

Generally, H0 states that the “null” condition exists; that is, there is nothing new or “interesting”

happening, the old theory is still true, the old standard is correct, or the system is in control

The alternative hypothesis, denoted by �1, is the opposite of the null hypothesis:

H1 covers all other possibilities; that is, it states that something “interesting” is happening, the

new theory is true, there are new standards, or the system is out of control

Level of significance (AKA threshold), denoted by �, is the chance we’re willing accept that we might

incorrectly reject the null hypothesis (recall: � is 1-Confidence Interval)

H0 always includes the concept of equality; for example:

H0The average over-budget amount= \$50,000

H0The average over-budget amount ≥ \$50,000

H0: The average over-budget amount ≤ \$50,000

The logical operator for H1 always expresses the logical opposite of the operator used for the null

hypothesis:

If the null expresses “=”, the alternative expresses “≠”

If the null expresses “≤”, the alternative expresses “>”

If the null expresses “≥”, the alternative expresses “<”

Decision Rule: along with formulating statistical hypotheses, rejection criteria, specifying α etc., we need

to specify our statistical test

We will look at three methods: • Critical Value method • Test Statistic method • P Value method

P value method: in this method, we calculate the p value

The null hypothesis is rejected if and only if the p value is smaller than α