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It's critical to test a hypothesis you have generated for a market research study. You also need to develop a second hypothesis to compare which of the two is supported by the study's data.

Our Hypothesis Testing Tool was created to help you test a hypothesis as part of a market research effort. The purpose of this tool is to help you set decision-making standards about the validity of sample results that apply to an overall population.

This Microsoft Word tool assists in assessing a null hypothesis and alternative hypothesis to identify which of the two is supported by the data gained through your research.

Key benefits and functionality include:

*standardizes hypothesis testing

*provides a professional format to document hypotheses

*evaluates data support for null & alternative hypotheses

*allows for easy communication among market research team

*encourages documentation of all market research efforts

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- 1. Hypothesis Testing Tool Purpose The purpose of this template is to help you test a hypothesis as part of a research effort. A hypothesis is an unproven assumption about a research topic. When doing research, one challenge is that one cannot know with certainty that what is true for a research sample is true for an entire population. Hypothesis testing is a means for setting decisionmaking standards about the validity of sample results that apply to an overall population. Marketing research is often triggered by a hypothesis. To test one hypothesis, a second, mutually exclusive hypothesis is developed, and research is conducted to determine which of the two is supported by the data. In research terminology, the hypothesis for which a proposed result is not true is known as the null hypothesis. The alternative hypothesis is the one for which the proposed result is true. No amount of research can prove beyond doubt that a hypothesis is true, but the research should lead to the rejection of one and the acceptance of the other. How to Use this Template Complete the following sections with your research team and/or stakeholders. Cut & paste this information into a document that reflects your corporate image. Use the information in this template and test a hypothesis.
- 2. Table of Contents 1. Hypothesis 3 2. Sample Statistic 3 3. Significance Level 4 4. Sample Data Analysis 5 5. Making a Decision 6
- 3. Notice the wording of the statements above: “would seem” to support the hypothesis. The degree to which you can have confidence in what the sample result says about the population depends on the significance level, which is discussed in the following section. What we must do now is determine which test or sample statistic to use to help us understand the relationship between the data. There are several kinds of statistical tests you can consider. In this example, the Z test makes sense. It is highly recommended that you use a statistical software package such as SAS or SPSS to help you determine which statistical test makes the most sense for your sample. Identify the sample statistic that you will use to test your hypotheses: Sample Statistic Record the sample statistic that you will use to test your hypotheses here. 3. Significance Level Having statistically significant results is important, so during this step, you will want to determine a significance level for the hypotheses you are testing. Statistical significance is an indicator whether the research results indicate a relationship between variables or is simply a product of chance. You will use statistical significance to accept or reject the null hypothesis. The significance level indicates the degree of error you’re willing to accept, and there are two kinds of errors that can occur during hypothesis testing: 1. The probability of rejecting the null hypothesis when it is actually true. In marketing research, this is known as Type I error. In our example, Type I error would occur because the results indicate that a 25% increase in advertising spending would produce at least a 20% increase in sales, when in fact the
- 4. 4. Sample Data Analysis Analyze the data and perform the necessary computations to derive the test statistic. In our example, let’s assume that we have a sample of 625 customers, 140 of which were exposed to at least 25% more advertising impressions than the rest of the sample, and whose purchases were at least 20% above the average customer purchase volume. Using this data, we can calculate the sample proportion, which is 140/625 that equals 0.224. Next, we use the sample proportion of 0.224 as input to the z test. Once again, we strongly recommend the use of statistical software, such as SAS or SPSS to help calculate the sample statistic, in this case, the z test. The results of the z test allow us to derive the p-value, which is the probability of obtaining a given result if the null hypothesis is true for the population. If the p-value is less than the selected significance level, the result is considered statistically significant. While there are formulas that will let do these computations manually, you’re far better off to use statistical analysis software such as SAS or SPSS to help you compute the p-value. In our example, the z test lets us determine that the calculated probability (p-value) of obtaining a sample proportion of 0.224 is 0.0668 when the null hypothesis is assumed true, and this p-value is higher than our pre-set significance level of 0.05. In the table below record the p-value and the significance level from the previous step: P-Value Enter computed p-value. Significance Level Enter significance level.

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