“Hypothesis testing is one of the fundamental concepts in both scientific research and business decision-making. It involves establishing a hypothesis (or guess!) about the outcome of an event or experiment and then gathering evidence (data) to make a decision on whether the hypothesis should be accepted or rejected” (Colorado Technical University Online, 2010a).

Null Hypothesis – H0

“The null hypothesis is the statement being tested. Often, the null hypothesis is a statement of ‘no difference’ or ‘no effect.’ The null hypothesis is not rejected unless there is convincing sample evidence that it is false” (Bowerman, O’Connell, & Orris, 2012). Say for example that a two liter of Coca-Cola is $1.89 at your local grocery store and you like Coke, not Pepsi, can’t even imagine drinking it. If the price went up to $2.19 would you still purchase the product? If the answer is yes, then the null hypothesis is that raising the price of Coke would not adversely affect customer loyalty and brand purchase. We would then test this theory in Step 2.

Alternative Hypothesis – H1

“The alternative, or research, hypothesis is a statement that will be accepted only if there is convincing sample evidence that it is true” (Bowerman, O’Connell, & Orris, 2012). Using the same example as above, but adding in a factor that you are on a tight budget, you decide not to purchase Coke or actually consider trying to learn to like drinking Pepsi. Then the alternative hypothesis is that raising the price of Coke will adversely affect customer loyalty and brand purchase.

Errors: Type I and Type II

“A Type I error is the error of rejecting the null hypothesis, H0, when it is in fact true. The probability of committing a Type I error is the risk level or alpha risk that the researcher specifies. A Type II error is the error of failing to reject the null hypothesis when it