How can statistics be misused

With the increase of global actions and advancement in technology, statistics has even more significance in analyzing various marketing strategies for businesses and much more.

The statistical data can help in solving various uncertainties of the business, help in better decision-making, make necessary judgments, and give more weightage to evidence. Apart from this, the statistics data is essential for proper planning so that a business can stand out effectively.

As the demand for statistics increases in this digital era, simultaneously, software and other advanced technologies are responsible for leading to the misuse of statistics. Yes, it is true that several people can misuse statistics information for their personal benefits. Misuse of statistics can be defined as the misuse of numerical information and misguiding people with certain details.

The incorrect information, or error in detail, or not providing full detail about the topics are some of the examples of misuse of statistics.

As per details of the promotion, several shoppers supposed Colgate as the best choice. But actually, it was not true. Therefore, it considers being a popular example of statistics to mislead. There are various types of statistics misusage, but below is the list of the most common misuse of statistics. And these are:. Keep in mind that people can misuse the statistics data by accident or by purpose.

There are several ways to misuse statistics in which certain parameters can be changed, or studies can be modified to represent the wrong information to the people. The major issue with the correlation is that the collected or analyzed data seems correlated or interrelated when it is taken on a large scale. This external pressure to find statistically significant results from research may bias some scientists to select a statistical method that is more likely to yield statistically significant results.

Additionally, the inclusion or exclusion of outliers, or even fabrication of data, may be justified in some scientists mind. There is a proposal for a registry of unpublished social science data that has statistically insignificant results The decision to analyze an exposure-outcome relationship should ideally be made prior to data collection, i. When analytical decisions are made a priori , the data collection process is more efficient and researchers are much less likely to find spurious relationships.

A priori analyses are needed for hypothesis testing, and are generally considered the stronger category of analytical decisions. Post-hoc or after the fact analyses can be useful in exploring relationships and generating hypotheses.

Often post-hoc analyses are not focused and include multiple analyses to investigate potential relationships without full consideration for the suspected causal pathway. The hazard arises when researchers perform post-hoc analyses and report results without disclosing that they are post-hoc findings. Based on the alpha level of 0. Proper disclosure of how many analyses were performed post-hoc , the decision process for how those analyses were selected for evaluation, and both the statistically significant and insignificant results is warranted.

Multivariate regression is often used to control for confounding and assess for effect modification Often when assessing the relationship between an exposure and outcome there are many potential confounding variables to control for through statistical adjustment in a multivariate model The selection of variables to include in a multivariate model is often more art than science, with little agreement on the selection process, which is often compounded by the complexity of the adjusting variables and theoretical relationships Reliance on a pre-determined set of rules regarding stoppage of the model building process can improve this process, and have been proposed since the s Directed acyclic graphs DAGs have been utilized to both mitigate bias and control for confounding factors DAGs hold strong potential for proper model selection, and may be a viable option for proper covariate selection and model creation Although there is no consensus on which method of model building is most appropriate, certain consistencies remain regardless of the model building method used.

Proper planning prior to data collection and well before analyses helps to ensure that variables are appropriately collected and analyzed. Biologically plausible factors based on the purported causal pathway between the exposure and outcome.

Other factors that the researcher may suspect would confound the exposure-outcome relationship. After identifying a comprehensive list of variables that may be effect modifiers or confounders, additional analytical elements need to be considered and decided upon.

Assessment for colinearity of variables and determination of treatment if colinearity is identified. Relatively few peer-reviewed articles contain any description of the number of variables collected, criteria for potential inclusion in a multivariate model, type of multivariate model building method used, how many potential variables were included in the model, and how many different assessments were performed.

Many statistical packages allow for a multitude of analyses and results, however proper interpretation is key to translation from research to practice. Understanding the implications of committing either a type I or type II error are key. Type I error is the false rejection of the null when the null is true.

Conversely, type II error is the false acceptance of the null hypothesis when the null hypothesis is false. Setting alpha levels prior to analyses are important; however, there are many elements that can influence the P-value, including random error, bias and confounding. A P-value of 0. Many researchers would argue that there may in fact be a relationship but the study was not able to detect it.

Additionally, committing a type II error can most often be influenced by bias and lack of sufficient statistical power. Complete understanding the implications of potentially committing either of these errors, as well as methods to minimize the likelihood of committing these errors should be achieved prior to beginning a study. There is increasing interest in improvement of statistical methods for epidemiological studies.

These improvements include consideration and implementation of more rigorous epidemiological and statistical methods, improved transparency and disclosure regarding statistical methods, appropriate interpretation of statistical results and exclusion of data must be explained. There are two initiatives aimed at biomedical researchers to improve the design, execution and interpretation of biomedical research.

Textbooks and biostatistical journals, including Biometrika, Statistical Methods in Medical Research, Statistics in Medicine, and Journal of the American Statistical Association, can provide up to date resources for application of statistical analytical plans, interpretation of results, and improvement of statistical methods.

Additionally, there are many statistical societies that hold annual meetings that can provide additional instruction, guidance, and insight. Furthermore, researchers should strive to stay informed regarding the development and application of statistical tests. Statistical tools including splines, multiple imputation, and ordinal regression analyses are becoming increasingly accepted and applied within biomedical research.

As new methods are evaluated and accepted in research, there will be an increasing potential for abuse and misuse of these methods. Perhaps most importantly, researchers should invest adequate time in developing the theoretical construct, whether that is through a DAG or simple listing of exposure measures, outcome measures, and confounders. There has been, and will likely continue to be misuse and abuse of statistical tools. Through proper planning, application, and disclosure, combined with guidance and tools, hopefully researchers will continue to design, execute and interpret cutting edge biomedical research to further our knowledge and improve health outcomes.

National Center for Biotechnology Information , U. Journal List Biochem Med Zagreb v. Biochem Med Zagreb. Published online Feb Matthew S. Thiese , Zachary C. Arnold , and Skyler D. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding author: Matthew S. Thiese ude. Received Nov 30; Accepted Jan 3. Copyright notice. This article has been cited by other articles in PMC. Abstract Statistics are the primary tools for assessing relationships and evaluating study questions.

However, the identifiable victim effect can be used strategically to counteract or override statistical evidence, in which case it should be measured against the evidence of the data. For instance, opponents of raising the estate tax might tell the story of a farming family who lost their farm due to taxes after the death of the original owner. While the story may be true, it is also worth noting that only about 0. After all, she says, vending machines are four times deadlier than shark attacks worldwide.

Graphs and graphics can simplify large data sets and make an argument more compelling and memorable. However, they can also distort the real significance of the data through the manipulation of scale and context or the omission of key data points. Make sure to look carefully at any graphs or graphics you might include in your presentation! Skip to main content. Choosing and Researching a Topic.

Therefore, it is important that they are not exposed, although their strategic support is crucial. Over the years, the statistical community has been able to identify the elements necessary to grant independence to NSOs. Those same principles and rules can be used for this body. As John, I believe it is time for action and the current times offer a window of opportunity to move forward.

It is also urgent. I expressed my concern about the situation of statistical legislations in Latin American countries and the need to promote its modernization as a first step towards the establishment of protective barriers to the independence of NSOs. There have been several cases of political interference, although not as notorious as the case of Argentina in the past.

In my intervention I mentioned the case of Ecuador, which statistical office INEC suffered unfortunate situations, and in the last 4 years had 6 different directors.

Ecuador is in a electoral period until April 11, and there is a risk that these bad practices will be considered normal by the next governments. The international community should say something, e.

It would be a good starting point to have concrete advocacy actions. I agree with Steve Macfeely that the UNSC side event has given the statistical community a lot more impetus to work out how best to act.

The event was very popular. Over people attended, most throughout the whole session, from all regions of the world. At a time when people are suffering from acute webinar fatigue that speaks to the importance of the issues discussed. Misuse of statistics is an abuse of power. The threats are wide-ranging and have been changing fast in recent years as the data revolution has gathered pace.

A good range of safeguards are in place and remain relevant but need further bolstering to be robust to the face of the changes taking place. There is unfinished business. A strengthened data ecosystem that does not tolerate misuse of statistics is needed at the national and global levels if the public good that could be generated is not to be seriously undermined.

We should cheer on those who produce statistics that serve the public good and call shame on those who suppress, distort, manipulate and misuse numbers to mislead, cover up and divert attention from what is really going on. There is an opportunity to give fresh impetus to efforts to implement a system of governance for statistics serving the public good that will serve the citizens of all nations.