hypothesis testing in business term paper

Often, one of the trickiest parts of designing and writing up any research paper is how to write a hypothesis. The entire experiment and research revolves around the research hypothesis (H1) and the null hypothesis (H0), so making a mistake here could ruin the whole design. Needless to say, it can all be a little intimidating, and many students find this to be the most difficult stage of the scientific method. In fact, it is not as difficult as it looks, and if you have followed the steps of the scientific process and found an area of research and potential research problem, then you may already have a few ideas. It is just about making sure that you are asking the right questions and wording your hypothesis statements correctly. The Three-Step Process Often, it is still quite difficult to isolate a testable hypothesis after all of the research and study. The best way is to adopt a three-step hypothesis; this will help you to narrow things down, and is the most foolproof guide to how to write a hypothesis. Step one is to think of a general hypothesis, including everything that you have observed and reviewed during the information gathering stage of any research design. This stage is often called developing the research problem. An Example of How to Write a Hypothesis A worker on a fish-farm notices that his trout seem to have more fish lice in the summer, when the water levels are low, and wants to find out why. His research leads him to believe that the amount of oxygen is the reason - fish that are oxygen stressed tend to be more susceptible to disease and parasites. He proposes a general hypothesis. “Water levels affect the amount of lice suffered by rainbow trout.” This is a good general hypothesis, but it gives no guide to how to design the research or experiment. The hypothesis must be refined to give a little direction. “Rainbow trout suffer more lice when.
Successful software systems from the combined effort and brainwork of a visionary business organization and a technically skilled delivery organization. Optimal solutions to the wrong problems are not likely to achieve the desired business results. Therefore, software projects need to broaden their scope and not only work on solutions but also commit to the discovery of the right problem. Strangely though, most software development methodologies focus only on how to produce new features and functions. But the difficulty lies not only in making the software per se, but also in finding out which problem to solve. We want to build what we can sell rather than sell what we can build! To accomplish this we shall introduce the art of hypothesis testing into the toolbox of the requirements engineer. Hypothesis testing comes from the world of science as a method of objectively determining the validity of uncertain claims. Then, by defining and building the smallest possible product, and shipping it as a hypothesis test, we can learn about our customers and avoid wasting our resources on futile products and focus our efforts on the ones that will achieve desired business results. Hypothesis Testing A business model reflects the intent of a company to deliver value to customers, entice customers to pay for value, and to convert those payments to profit. At design-time, a business model is just a chain of hypotheses about the cause and effect relationships that enable the long-term business effects. It might look good on paper and the work put into its design might be substantial, but it does not make it a solid foundation to craft new businesses from. Yet, high level models are strikingly often taken as hard fact, while in reality, it is ’terra incognita’ and needs to be tested incrementally. The problem is that people tend to stick to their beliefs and defend, justify, and.
Photo by: olly Social science research, and by extension business research, uses a number of different approaches to study a variety of issues. This research may be a very informal, simple process or it may be a formal, somewhat sophisticated process. Regardless of the type of process, all research begins with a generalized idea in the form of a research question or a hypothesis. A research question usually is posed in the beginning of a research effort or in a specific area of study that has had little formal research. A research question may take the form of a basic question about some issue or phenomena or a question about the relationship between two or more variables. For example, a research question might be: Do flexible work hours improve employee productivity? Another question might be: How do flexible hours influence employees' work? A hypothesis differs from a research question; it is more specific and makes a prediction. It is a tentative statement about the relationship between two or more variables. The major difference between a research question and a hypothesis is that a hypothesis predicts an experimental outcome. For example, a hypothesis might state: There is a positive relationship between the availability of flexible work hours and employee productivity. Hypotheses provide the following benefits: They determine the focus and direction for a research effort. Their development forces the researcher to clearly state the purpose of the research activity. They determine what variables will not be considered in a study, as well as those that will be considered. They require the researcher to have an operational definition of the variables of interest. The worth of a hypothesis often depends on the researcher's skills. Since the hypothesis is the basis of a research study, it is necessary for the hypothesis be developed with a great deal of thought and.
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This paper demonstrates that there is currently a widespread misuse of two-tailed testing for directional research hypotheses tests. One probable reason for this overuse of two-tailed testing is the seemingly valid beliefs that two-tailed testing is more conservative and safer than one-tailed testing. However, the authors examine the legitimacy of this notion and find it to be flawed. A second and more fundamental cause of the current problem is the pervasive oversight in making a clear distinction between the research hypothesis and the statistical hypothesis. Based upon the explicated, sound relationship between the research and statistical hypotheses, the authors propose a new scheme of hypothesis classification to facilitate and clarify the proper use of statistical hypothesis testing in empirical research.KeywordsHypothesis testing; One-tailed testing; Two-tailed testing; Statistical hypothesis; Research hypothesis in existential form; Research hypothesis in non-existential form1. IntroductionStandard textbooks on statistics clearly state that non-directional research hypotheses should be tested using two-tailed testing while one-tailed testing is appropriate for testing directional research hypotheses (e.g., Churchill and Iacobucci, 2002 and Pfaffenberger and Patterson, 1987). However, during the actual conduct of statistical testing, this advice is not often heeded. According to our observation of 492 recent empirical articles that have used structural equation modeling (SEM), regression analysis, and analysis of variance (ANOVA) in five selected marketing research-related journals, the Journal of Marketing, Journal of Marketing Research, Marketing Science, Journal of Consumer Research, and Advances in Consumer Research (2001–2005), there were 2703 (N = 2703) research hypotheses in total. Overall, 90.9% (n = 2458) of them are expressed in directional form, but.