Most businesses are gambles, characterized by guts, intuition and boldness. However, wise business owners research before making crucial business decisions. Proper research should start with a reasonable hypothesis, which is simply a brief statement that makes a prediction based on some observations. Reasonable hypotheses lead to better decisions that assist in the achievement of business objectives (List et al., 2019). For instance, when a decision-maker needs to determine how a price increase will affect the customer base or how much should be spent on advertising, it is easy for them to get lost in analysis paralysis or make wild assumptions. Good business hypotheses solve such problems since they are initially based on some basic information. Therefore, hypotheses based on several years of research in a specific business area can help a business to direct its research appropriately. Such hypotheses predict the relationship between two variables. As a result, the business will not waste resources and time studying unnecessary variables. Hypothesis testing has many uses in business development. Before making crucial decisions in business development, business owners can experiment with hypothesis tests to be more confident in their decisions. Hypothesis testing also enables business researchers to analyze their data correctly. Through hypothesis testing, businesses can easily make decisions in areas such as determination of business location, cost of customer acquisition, effects of prices on customers and marketing budgets. In a nutshell, hypothesis testing is a simple process that helps a business to make better decisions.
Findings
Chi-square tests were conducted based on a null hypothesis. The null hypothesis is: There is no relationship between the number of sales that a representative makes and the type of territory (defined or open) that a representative makes. For this test, the critical value was 3.841. The calculated chi-squared values were as follows.
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Southeast Region – 0.2
Northeast Region – 36.93
Midwest Region – 2.512
Pacific Region – 30.545
The Chi-squared test requires that the null hypothesis be rejected when the chi-squared value is higher than the critical value (Bozeman Science, 2011, 04:22). The null hypothesis should be accepted when the chi-squared value is lower than the chi-squared value is lower than the critical value. From the test, the Southeast and the Midwest region have chi-squared values lower than the critical value. For these two regions, the company should accept the null hypothesis.
On the other hand, the chi-squared values for the Northeast and Pacific regions are higher than the critical value. For these regions, the company should reject the null hypothesis in favor of the alternate hypothesis. A chi-squared value that is higher than the critical value indicates that there is a relationship between the kind of sales territory a sales representative has and the number of sales he or she makes during the month.
Other Statistical Analyses
Besides the chi-square analysis, there are other statistical analyses the company can conduct before deciding whether to go with a defined or open sales strategy. For instance, the company may choose to do a statistical forecasting analysis before selecting a sales strategy. In the establishment of new businesses or the adoption of new business strategies, it necessary to determine future success (Hyndman & Athanasopoulos, 2018). Numerous risks and certainties characterize the business environment. With the help if forecasting, the company can determine which strategy will ensure its success. The correctness of such a decision largely depends on accurate forecasting (Hyndman & Athanasopoulos, 2018). The company should, therefore, contemplate forecasting sales based on both the open and defined sales strategies. The forecast should include the projection of prices, use of past data and consideration of factors that may affect sales.
The company may also consider performing a regression analysis. Regression analysis is a reliable means of determining the variables that impact a given topic of interest (Gunst, 2018). In this case, the company may decide to conduct a regression analysis to determine how the number of sales and the sales strategy are related. For this analysis, the sales strategy may be the independent variable, and the number of sales may be the dependent variable. There will be a need to establish a comprehensive set of data that will be used to perform the analysis. Regression analysis is a useful statistical method that can be used to determine the extent to which a specific independent variable influences a dependent variable (Gunst, 2018).
Furthermore, the company may also consider determining the measures of central tendency. Notably, the company should consider determining the mean sales for all regions for both strategies. This value will indicate which strategy generally has a higher value for the average sales in an area.
Applications of Chi-Square Test
The chi-square can also be used in other business applications other than determining the best sales strategy. For example, a business may need to assess the effect of a price increase on customers. I most cases, companies may need to raise prices of goods and services to increase profitability. However, such moves always have consequences, especially on the customer base. Particularly, most cases end up with a business losing customers. Before a business decides to hike its prices, it may, therefore, be necessary to conduct a hypothesis test on whether a price increase will reduce the customer base. For such a scenario, the business may develop a null hypothesis. The hypothesis may be stated as: There is no relationship between a price increase and the number of customers. The alternate hypothesis may be: There is a relationship between price increase and the number of customers. After the formulation of the hypothesis, the business may then gather relevant data and conduct the chi-square test to determine whether the null hypothesis is true or not. By doing a Chi-square test, the company may decide on whether to increase prices or not.
Another scenario where the Chi-square test may be useful is in the determination of a business location. The location of a business depends on several factors, including the customer population. A company needs to determine an area with a vast customer base. To determine whether a business location determines the number of customers, a business may decide to determine the number of customers available in different places and compare this data with the number of customers it expects in these locations. After this data is obtained, the business can then conduct a Chi-square test based on the null hypothesis: The business location does not affect the number of customers. By determining the chi-squared value and comparing it with the critical value, the business can evaluate the effect of its location on the number of customers. Consequently, the leaders will make informed decisions on the best location for the company.
Finally, the Chi-square test can be used by businesses in the evaluation of marketing strategies. Before selling a product, a company may need to determine the relationship between different factors that determine the performance of a product in the market. A company that sells different kinds of products to various regions may need to determine whether a relationship exists between the different areas where the company has stores and the product category. The company may conduct a Chi-square test based on the null hypothesis: There is no relationship between the location of stores and the product category that customers prefer. By performing the Chi-square test, the company will know what products to market in different regions, which will save both money and time.
References
Bozeman Science. (2011, November 13). Chi-squared test [Video file]. Retrieved from https://youtu.be/WXPBoFDqNVk
Gunst, R. F. (2018). Regression analysis and its application: a data-oriented approach . Routledge.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice . OTexts. (Hyndman & Athanasopoulos, 2018)
List, J. A., Shaikh, A. M., & Xu, Y. (2019). Multiple hypothesis testing in experimental economics. Experimental Economics , 22 (4), 773-793.
