In the manuscript “The Use and Misuse of Statistics” It is interesting to find out that only 5 percent of the managers’ works are represented in the actual statistical calculations. The remaining 95 percent is used to get the right calculations, also used to interpret the results. Managers think that they do much number crunching whereas they do not. In fact, they have only an idea of crunching primarily, and they spend their time persuading people on their assertions (Fayoyin, & Ngwainmbi, 2017). The managers do not realize how their assertions are based on assumptions that are unproven.
In the Teds Talks- Battling Bad Science it is interesting to note that people have problems with reports where they are not sure whether the research is good or not and hence find it hard to believe in it. The reports do not carry any evidence to convince someone on what is written in them. It is also important to note that primary sources which most people rely on are not interpretive and not factual. Also in this talk it is interesting to note that all that we feed on cause and prevent cancer at the same time, this is unbelievable anyway.
An example when I was exposed to sick science is when I was told the body could take in 40 milligrams of aluminum without causing damage. However, if you eat meat that’s been cooked in foil, you could be ingesting more than six times that suggested an amount.
Differences between experimental, quasi-experimental and non-experimental research
In experimental research, experiments are carried out to come up with conclusion whereas in nonexperimental no experiment is carried out (Glanville, et al., 2017). Experimental research can show the cause and effect whereas the nonexperimental research cannot. Experimental strategy creates groups by manipulating the independent variable. On the other hand, both quasi-experimental and non-experimental use non-manipulated variables to define groups. The difference between quasi-experimental and non-experimental research is that Quasi-experimental research attempt to control or limit threats to internal validity whereas non-experimental research does not (Becker, et al., 2017).
Becker, B. J., Aloe, A. M., Duvendack, M., Stanley, T. D., Valentine, J. C., Fretheim, A., & Tugwell, P. (2017). Quasi-experimental study designs series—paper 10: synthesizing evidence for effects collected from quasi-experimental studies presents surmountable challenges. Journal of clinical epidemiology, 89, 84-91.
Fayoyin, A., & Ngwainmbi, E. K. (2017). Use and Misuse of Data in Advocacy, Media, and Opinion Polls in Africa: Realities, Challenges, and Opportunities. In Citizenship, Democracies, and Media Engagement among Emerging Economies and Marginalized Communities (pp. 325-345). Palgrave Macmillan, Cham.
Glanville, J., Eyers, J., Jones, A. M., Shemilt, I., Wang, G., Johansen, M., … & Rothstein, H. (2017). Quasi-experimental study designs series—paper 8: identifying quasi-experimental studies to inform systematic reviews. Journal of clinical epidemiology, 89, 67-76.