Using statistics in a scientific study requires a lot of planning. Learn how anecdotal evidence is the opposite of statistical methodology and how it can lead you astray! Use it to protect yourself from manipulation and to react to information intelligently. Statistical knowledge is the solution to this problem. But, how do you know? If you’re not familiar with statistics, these manipulations can be hard to detect. That long list of accidental pitfalls can quickly become a source of techniques to produce misleading analyses intentionally. Unscrupulous analysts can use incorrect methodology to draw unwarranted conclusions. I’m sure you’re familiar with the expression about damned lies and statistics, which was spread by Mark Twain among others. Lies, Damned Lies, and Statistics: Use Statistical Knowledge to Protect Yourself My interests are quite broad, and statistical knowledge provides the tools to understand all of them. “The best thing about being a statistician is that you get to play in everyone else’s backyard.” One of my favorite quotes about statistics is the following by John Tukey: Furthermore, statistical careers often pay quite well. Chances are very high that you’ll need working knowledge of statistical methodology both to produce new findings in your field and to understand the work of others.Ĭonversely, as a statistician, there is a high demand for your skills in a wide variety of areas: universities, research labs, government, industry, etc. Even if the field of statistics is not your primary field of study, it can help you make an impact in your chosen field. Statistical analyses are used in almost all fields to make sense of the vast amount of data that are available. Statisticians can guide others through this swamp! Use Statistics to Make an Impact in Your Field Unfortunately, there are many ways to mess up analyses and produce misleading results. Numerous considerations must be correct to produce trustworthy conclusions. Fastidious statisticians track the number of tests performed during a study and place the results in the proper context. When analysts conduct many tests, some will be statistically significant due to chance patterns in the data. When you violate an important assumption, you risk producing misleading results.ĭata mining: Even when analysts do everything else correctly, they can produce falsely significant results by investigating a dataset for too long. Adding to the complexity, you can waive some assumptions under specific conditions-sometimes thanks to the central limit theorem.
These assumptions often involve properties of the sample, variables, data, and the model. Violating the assumptions for an analysis: Most statistical analyses have assumptions. Incorrect analysis: Are you analyzing a multivariate study area with only one variable? Or, using an inadequate set of variables? Perhaps you’re assessing the mean when the median might be a better? Or, did you fit a linear relationship to data that are nonlinear? You can use a wide range of analytical tools, but not all of them are correct for a specific situation.
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Learn how to determine whether you’re observing causation or correlation! When A precedes B, and A is correlated with B, many mistakenly believe it is a causal connection! However, you’ll need to use an experimental design that includes random assignment to assume confidently that the results represent causality. The following list provides a small taste of potential problems and analytical errors that can affect a study.Ĭausality: How do you determine when X causes a change in Y? Statisticians need tight standards to assume causality whereas others accept causal relationships more easily. A single weak link might produce unreliable results. The overall quality of the results depends on the entire chain of events. In some cases, you might want to take the raw data and use it to cluster observations in similar groups by using patterns in the data to help target your research or interventions. This process includes constructing the study design, selecting and measuring the variables, devising the sampling technique and sample size, cleaning the data, and determining the analysis methodology among numerous other issues. Using statistical analyses to produce findings for a study is the culmination of a long process. Statisticians Know How to Avoid Common Pitfalls
Statisticians ensure that all aspects of a study follow the appropriate methods to produce trustworthy results.
In fact, statistical analyses account for uncertainty and error in the results. When analysts use statistical procedures correctly, they tend to produce accurate results. Along the way, statisticians can help investigators avoid a wide variety of analytical traps. Statisticians offer critical guidance in producing trustworthy analyses and predictions.
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