The Impact Of Traveling On Human Hydration Rates


This assignment focuses on the amount of water that I drank while on my trip. I have collected statistical data about how many ounces of water I consumed over five days. In a previously completed paper, I relied on descriptive statistics to manipulate this data. I found the mean, mode, median, variance, and standard deviation. These values helped me understand the data and summarize the essential features of the selected dataset. However, one should admit that descriptive statistics are not sufficient for uncovering the complete meaning of what these water amounts imply. This statement denotes that it is necessary to develop a specific research question that will allow for formulating the null and alternative hypotheses. The latter will then be used to guide research and find any reasonable conclusions regarding a specific topic. Thus, the present assignment attempts to answer the following question: Does traveling affect people’s hydration rates? This request can generate the null and alternative hypotheses that are presented below.

Null and Alternative Hypotheses

Research typically determines whether and how various processes, interventions, or phenomena have any impact. In particular, this project focuses on whether traveling affects people’s behavior in terms of how much water they consume. A null hypothesis is then formulated to predict a possible answer to the articulated question. One should admit that null hypotheses are always statements that an intervention, process, or phenomenon does not have any effect on the population. For this assignment, a null hypothesis is as follows:

H0 – Traveling does not affect the amount of water that people consume and, therefore, does not impact people’s hydration levels.

However, there is no doubt that all research activities are taken because some effects and outcomes are expected. Scientists and researchers typically hope to receive specific results, and an alternative hypothesis is used to present these expectations. In many cases, an alternative hypothesis is the same as a leading research hypothesis that a particular project tests. The current assignment relies on the following null hypothesis:

HA – Traveling affects the amount of water that people consume, which leads to either lower or higher hydration rates.

Hypotheses Discussion

It is reasonable to emphasize that the hypotheses above were selected because they are appropriate for the collected data and, therefore, the research topic. Once it is determined what issue or phenomenon a project investigates, it is necessary to articulate statements that will present mutually excluding outcomes. This information demonstrates that when one hypothesis is proved, the other one is automatically considered false. In particular, if researchers fail to prove that traveling leads to either lower or higher hydration rates, they can claim that the null hypothesis above is defended. In this case, it is not possible to make any inferences from the research about the population.

It is challenging to overestimate the significance of a null hypothesis in research. It should be included in every research because it presents a possible outcome of the entire project. Thus, this statement is an essential formality that cannot be eliminated from projects. However, one should additionally explain that the null hypothesis concept is associated with the p and values. Aczel et al. (2018) stipulate that “one is entitled to reject the null hypothesis whenever the p-value is smaller than or equal to a predefined threshold (typically set at.05)” (p. 357). This approach is also known as Null Hypothesis Significance Testing (NHST). Sedgwick et al. (2022) clarify that the p-value refers to the statistical significance that is called a gold standard for determining whether an intervention under analysis can be considered effective. These two values are associated with inferential statistics that are necessary for making significant conclusions about the interventions and phenomena under investigation.

However, the information above leads to a particular issue that deserves attention. According to Aczel et al. (2018), if the p-value is higher than a (typically.05), it is impossible to reject the null hypothesis. This statement further leads to the conclusion that a selected intervention does not affect the population. However, this approach is overly categorical, and various researchers acknowledge this issue. Sedgwick et al. (2022) indicate that reliance on the NHST can result in false claims about the ineffectiveness of specific interventions. This problem occurs when researchers fail to prove that the obtained results meet the predetermined threshold. As a consequence, these authors are forced to claim that an intervention or process does not impact the population. One should highlight that the real state of affairs can be different because Aczel et al. (2018) analyzed 137 studies with nonsignificant results and found that fewer than 5% of these articles had sufficient evidence to reject their null hypotheses. That is why researchers should be careful with interpreting the results concerning the null hypothesis.


According to the collected data, this assignment has articulated the null and alternative hypotheses. These statements are essential since they reveal what outcomes can be obtained while analyzing the data further. Even though the null hypothesis concept is important, various scientists indicate that its use can lead to false conclusions regarding the interpretation of findings. Thus, researchers should carefully approach data analysis to make reasonable and valuable conclusions.


Aczel, B., Palfi, B., Szollosi, A., Kovacs, M., Szaszi, B., Szecsi, P., Zrubka, M., Gronau, Q. F., van den Bergh, D., & Wagenmakers, E. J. (2018). Quantifying support for the null hypothesis in psychology: An empirical investigation. Advances in Methods and Practices in Psychological Science, 1(3), 357-366.

Sedgwick, P. M., Hammer, A., Kesmodel, U. S., & Pedersen, L. H. (2022). Current controversies: Null hypothesis significance testing. Acta Obstetricia et Gynecologica Scandinavica, 101(6), 624-627.