Challenges in hypothesis formulation: to reject or accept?

A hypothesis starts out with a researcher having initial hunches with he/she attempting to answer a specific research question. Watson (n.d. cited Bryman and Bell, 2011) and Newby, 2010 defines a hypothesis as a testing of the possible relationship between two or more variables in order to answer a research speculation. To accept or to reject a hypothesis is the criteria that circulate around the quantitative style of research (Tahir, n.d.), while probabilities and likelihoods are at the centre of the decision process when accepting or rejecting a hypothesis (Newby, 2010) based on the evidence reflected by the data representing the initial research question. Some of the general challenges a researcher may face in the beginning of crafting their hypothesis are how to phrase and formulate questions to answer your hypothesis, especially for an unseasoned researcher. Bryman and Bell (2011) encourage researchers to refer to other journal articles, dissertations and researchers who have carried out research to answer similar questions, and use them as a guide to formulate your hypothesis. However, the author views this as a minor challenge, and tougher challenges may emerge throughout the process of accepting a hypothesis.

The qualitative style of research consists of many decisions that have to be made, thus creating a high probability for mistakes or errors. Ghanem (2003) proposes researchers follow the mechanism model when leading to the decision of accepting or rejecting a hypothesis, which includes three basic steps to minimise these errors. The first step is hypothesis formulation — where the aim of this stage is to “produce a proposed scientific hypothesis as a tentative explanation to the phenomenon in question” (ibid); followed by hypotheses evaluation stage — where researchers may reconsider and develop alternative hypothesis is the working hypotheses may not be suitable when tested; the last step is the hypothesis verification stage — this phase aims for the clarification of the final hypothesis which is done by carrying out either or combined of these three scientific methods: research, observation or experimenting.

While the general flow for formulating hypotheses using the hypothetico-deductive approach is very similar. The author postulates that this step may be comparable or synonymous to the verification stage in the mechanism model. The last step in the hypothetico-deductive approach suggests that although a positively tested hypothesis is more enticing a negative finding of a hypothesis may still be worth the effort during the testing of the hypothesis (Fisher, 2010). However, the author would recommend researchers to be very certain about their findings before concluding their findings as a negative result, as it could lead to two potential errors. Type error 1 — which is the discarding of the false negative result, and type error 2 — which is when a false hypothesis is accepted (ibid). The author analyses that this is one of the harder challenges when formulating a hypothesis. When will a researcher be able to identify if a negative result is false? Moreover, if a researcher does not establish a hypothesis as false, and accept the hypothesis, the whole research is jeopardised. Therefore, the author fears that drawing the wrong conclusion may be one of the biggest challenges. In addition, not all researchers may have time to redesign a hypothesis due to time and capability, for example a Master’s dissertation (Fisher, 2010). The author postulates this issue may arise due to the relationship between variables, which may be more complicated than expected. While there are obstacles to face when verifying hypothesis, there are other challenges to face in the formulation stage of the mechanism model.

Ghanem (2003) suggests that one way to minimise the probability of rejecting a hypothesis relates back to the sampling method. For example, if there were 300 couples on a vacation holiday to provide a researcher with the information on booking platforms, each couple should be given the same 1 in 300 chance of providing the researcher with information. This randomness reduces the risk of bias, therefore resulting in more accurate data. The author draws the connection that supports the previous chapter in false hypothesis. The author suggests that although bias may work in favour of data aligning to the hypothesis desired by the researcher, this breaches research ethics and may result in producing a false hypothesis. This preventive step may help to avert accepting “false” hypothesis. Moreover, Newby (2010) support Ghanem, stating that obtaining poor data may be one of the difficulties that a researcher may face, especially when the researcher will draw conclusions from the analysis collected. One way to counter this is to pay attention to the method of data collection (Ghanem, 2003; Newby, 2010). Even if the researcher has chosen the most appropriate sample size and method, the sample could still be imbalanced. For instance, it a questionnaire contains sensitive questions; the sample may falsify the results and/or refuse to answer that particular question. One method to counter this issue is to be very careful in phrasing and constructing the questions in the questionnaire, and ensuring the questions are diplomatic and inoffensive.

Quantitative research appears very circumstantial. As hypothesis is the testing of possible relationships, Newby (2010) suggests “the nature of proof is the second issue that makes quantitative research distinctive”. The author agrees with this statement, when formulating a hypothesis, how would a researcher really know if the patterns we draw upon really exist? How do researchers prove to fellow academics that the findings are true? Perhaps the only possible process of validating a hypothesis is through many rounds trial and error. A researcher has to look at many aspects before accepting or rejecting hypotheses.

Bryman, A. and Bell, E. (2011) Business Research Methods. 3rd ed. Oxford: Oxford University Press.

Fisher, C. (2010) Researching And Writing A Dissertation: An Essential Guide For Business Students. 3rd ed. England: Pearson Education Limited.

Ghanem, T. (2003) The Process of Formulating Hypotheses and Students’ Difficulties of Hypotheses Formulation in Science Learning. Available from: [Accessed 3 December 2015].

Marshall, C. and Rossman, G. B. (2011) Designing Qualitative Research. 5th ed. Los Angeles: SAGE Publications.

Tahir, S. Z. B. (n.d.) Hypothesis Formulation. Available from: [Accessed 4 December 2015].