Statistical Evidence: The Reliability Analysis


Research methods provide several solutions to obtain information, make inferences about factors underlying a particular phenomenon. However, compared to qualitative information that is based on individuals’ judgment and applied theories, quantitative information or statistical data draws information from factual figures that do not represent a researcher’s ideas. As a result, people believe that statistical evidence represents the truth because they are measurable. Even so, the author of Can People Lie with Statistics suggests that numbers and figures do not always represent the truth because they still depend on several human factors. Thus, the arguments provided are reasonable as researchers can make mistakes or exhibit bias when presenting the information.

Reasons Why People are Quick to Accept Statistical Facts as True

Scientists, researchers, and educators consider statistics as the final source of proof because they comprise actual numbers that support particular ideas and conclusions. As a result, numbers are widely used as pieces of evidence because they point to a specific set or subsets of the components in question (Van der Bles et al., 2019). For example, a researcher sharing information about how students in the US are unfamiliar with the geographic map might not sound believable without incorporating data. Therefore, stating that 75% of the student population cannot find Washington or another state on a map makes the argument difficult to refute since it provides an actual estimate and establishes its point as true.

Additionally, numerical data is obtained from studies that reveal connections between various facts. In other words, statistical evidence provides headcounts and ensures that all components are factored in to generate conclusions. As a result, they represent valid concepts and results that can be repeated in different investigations (Núñez-Núñez et al., 2022). Compared to qualitative information that depends on the observers’ judgment and is at risk of bias, researchers obtain statistical data using calibrated measuring instruments. Therefore, samples obtained from numerical figures are preferred for making decisions because of their validity.

Reasons Why Spinning the Truth is Unacceptable in Research

Spinning the truth in research is an unethical and unacceptable vice with adverse implications due to the role of studies and their essence in human development. Researchers seek to identify various factors in the human environment and their relationships with each other to guide policy-making, decision-making, and changes toward achieving better outcomes (Van der Bles et al., 2019). Therefore, spinning the truth means that the information passed to individuals is not representative of the phenomenon being investigated and will not yield the expected results if the data is used to guide developments. Hence, the practice is not ethical because it undermines the value and accuracy of research data.

Spinning data obtained from studies can be disastrous when trying to advance social change, especially when utilizing financial resources. Distorting information in favor of some components over others limits the equal distribution of resources and may introduce complications to a system, thus resulting in its failure (Núñez-Núñez et al., 2022). For example, altering information on social indicators such as the rates of disease and poverty can encourage policy-makers to focus on areas that do not require attention while neglecting critical issues. In the long run, project initiatives become ineffective and individuals suffer more because of lacking access to basic requirements. Therefore, research governing bodies should establish strict rules and evaluation procedures to identify biases and errors intended to mislead individuals in studies.

Russian Military Kills 397 Children in Ukraine

Russia has waged war in Ukraine since February this year with the death toll from the conflicts continuously rising due to occasional attacks and retaliations. A news article by Kata (2022) suggests that since the beginning of the war, the Russian military has killed 397 children. Although these statistics might be true, they exhibit high levels of bias because most sources of information regarding the war depend on rough estimates provided by individuals on the site. In addition, the news article does not offer substantial information about where it retrieved the data. Moreover, it does not clarify whether the number provided is the total number of children who have lost their lives due to the conflict. Thus, it has several gaps that limit its accuracy and validity.

Military engagement between Russia and Ukraine has resulted in unprecedented adversities. The war between the two nations disrupted social systems and prevented individuals from living a high-quality life. Therefore, the number of children killed in the conflict might be significantly higher because individuals have been losing their lives due to different reasons. Apart from direct conflicts, children in Ukraine are exposed to contagious diseases, poor sanitation, and hunger. Thus, more children might have died due to these reasons, making it critical to factor in various components to obtain more meaningful results.


Researchers and scientists champion the use of evidence-based information to oversee social change and positive advancements. Nevertheless, the reliability of some resources might be questionable because of investigators’ mishaps of spinning data. Distorting information is an unethical habit and a vice that can lead to adverse outcomes, especially in social issues. Therefore, it is critical to developing solutions to limit researchers from spinning data and presenting inaccurate facts and figures. Although statistical data is more reliable than qualitative data, it is important to include information from reliable sources to ensure that the studies achieve their intentions.


Kata, S. (2022). Some 397 children killed due to Russian aggression against Ukraine.

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Núñez-Núñez, M., Andrews, J. C., Fawzy, M., Bueno-Cavanillas, A., & Khan, K. S. (2022). Research integrity in clinical trials: innocent errors and spin versus scientific misconduct. Current Opinion in Obstetrics and Gynecology, 34(5), 332-339.

Van der Bles, A. M., Van Der Linden, S., Freeman, A. L., Mitchell, J., Galvao, A. B., Zaval, L., & Spiegelhalter, D. J. (2019). Communicating uncertainty about facts, numbers and science. Royal Society open science, 6(5), 181870.