Most common statistical methods assume that each unit of analysis is an independent measurement. It is also important to note that appropriate use of specific statistical tests depends on assumptions or assumed characteristics about the data. One of the greatest pitfalls of statistics is that the average person does not understand them AT ALL!!! Subscribe here: Statistics professor Walter Krämer, Technical University Dortmund. Basic science studies are complex because they often span several scientific disciplines. Here are 15 places with outstanding characteristics. If variables are not normally distributed or are subject to extreme values (eg, cholesterol or triglyceride levels), then medians and interquartile ranges (calculated as Q3−Q1, in which Q indicates quartile) are more appropriate. ANOVA is robust for deviations from normality when the sample sizes are small but equal. There is often confusion about when to present the standard deviation or the standard error. Data sets have errors from multiple sources, e.g., faulty instrumentation, transcription errors, cut and paste mistakes. Walter Krämer is Professor for Statistics in Dortmund and knows which facts best describe Germans, and which don’t. A key feature of survival data is censoring, which occurs when some experimental units do not experience the event of interest (eg, development of disease, death) during the observation period. In clinical studies, the first summary often includes descriptive statistics of demographic and clinical variables that describe the participant sample. Jetzt einloggen Kostenlos registrieren ★ PREMIUM-INHALT. The 9 Pitfalls of Data Science is the modern version of the classic book, How to Lie with Statistics. Pitfalls of Ranking; Home > Crime Info & Support > Crime Information Center > Crime Statistics > Pitfalls of Ranking. In such a case, the observed effects can be used to design a larger study with greater power. The outcome of interest is cell protein (a continuous outcome), and the comparison of interest is the change in cell protein over time between strains. Again, multiple mice are used to grow a large number of cells that are then frozen in aliquots. Mean and standard error of systolic blood pressure (SBP) by type. They find that until 31 March 2020, deaths in Italy increased by 39% or 25,354 compared to the average of the five previous years. Readers are going to be most interested in studies that uncover interesting, and new non-zero relationships. The issues addressed are seen repeatedly in the authors' editorial experience, and we hope this article will serve as a guide for those who may submit their basic science studies to journals that publish both clinical and basic science research. National Center Exceptions are their love of cars, their love of their homeland and their enthusiasm for football. In contrast, basic science studies are often handled less uniformly, perhaps because of the unique challenges inherent in this type of investigation. The research presented here provides examples of how the occurrence of statistical downscaling pitfalls can vary geographically, with time of year, climate conditions, and across SD techniques. Sample sizes are often quite small and are not likely to support formal statistical testing of the underlying hypothesis. Will comparison groups, for example, be independent (eg, experimental units randomized to competing conditions) or dependent (the same units measured under each experimental condition, sometimes called a matched, paired, or repeated‐measures design)? We wish to compare cell protein as an index of cell growth in fibroblasts from 2 different strains of mice (wild type and TG) after fibroblasts are plated and allowed to grow for 0, 1, 3, 5, 7, and 9 hours. It is more appropriate to clearly indicate the exact sample size in each comparison group. Many statistical tests are robust, meaning that they work well not only when assumptions are met but also when there are mild departures from assumptions. Several statistical comparisons are of interest. The procedures differ in terms of how they control the overall type I error rate; some are more suitable than others in specific research scenarios.7, 8 If the goal is to compare each of several experimental conditions with a control, the Dunnett test is best. A critically important first step in any data analysis is a careful description of the data. Investigators might observe mice for 12 weeks, during which time some die and others do not; for those that do not, the investigators record 12 weeks as the last time these mice were observed alive. The hardest errors to spot are the ones that don't look like errors at all. Concurrent control groups are preferred over historical controls, and littermates make the best controls for genetically altered mice. In basic science research, investigators often have small sample sizes, and some of their statistical comparisons may fail to reach statistical significance. Some experiments may involve a combination of independent and repeated factors that are also sometimes called between and within factors, respectively. But in per capita beer consumption they have been overtaken by the Czech Republic and Austria. Indeed, statistics is perhaps more open to misuse than any other subject, particularly by the nonspecialist. Consequently, there are multiple reasons why the statistical analysis of basic science research might be suboptimal. We wish to compare organ blood flow recovery over time after arterial occlusion in 2 different strains of mice. In the case of averages it’s always important to keep the deviations in mind. In basic science studies, investigators often move immediately into comparisons among groups. It is common to see investigators design separate experiments to evaluate the effects of each condition separately. In such cases, we recommend that investigators consider a range of possible values from which to choose the sample size most likely to ensure the threshold of at least 80% power. Investigators can limit type I error by making conservative estimates such that sample sizes support even more stringent significance criteria (eg, 1%). This value is a censored time and is less than the time to event, which will occur later (and is unmeasured). Statistical results are not always beyond doubt: “Statistics deals only with measurable aspects of things and therefore, can seldom give the complete solution to problem. When hypothesis testing is to be performed, a sample size that results in reasonable power (ie, the probability of detecting an effect or difference if one exists) should be used. Stratification is a means to combat bias and confounding. "The 9 Pitfalls of Data Science is the modern version of the classic book, How to Lie with Statistics. PUBLIC SPENDING by Evan Davis . Pitfall 3: Ignoring the effects of statistical power. Investigators often design careful studies with repeated measurements over time, only to ignore the repeated nature of the data with analyses performed at each time point. If the sample size is relatively small (eg, n<20), then dot plots of the observed measurements are very useful (Figure 1). Or from where the most expats come? With larger samples, however, summary measures are needed. This shows that the banks’ value chain is increasingly distributed across supplier industries and also that statistics have their pitfalls. One of the major pitfalls with relying heavily on statistical significance is that it leads to publication bias. And the average number of spectators per match in the Bundesliga is higher than any other top league in Europe. In every study, it is important to recognize limitations. In contrast, the 12 repeated measures of weight could be used to assess the accuracy of the mouse weights; therefore, the 12 replicates could be averaged to produce n=1 weight for each mouse. Now let’s define two different zoning schemes: one which follows a uniform grid pattern and another that does not. Although determining an appropriate sample size for basic science research might be more challenging than for clinical research, it is still important for planning, analysis, and ethical considerations. Note that analyses at each time point would not have addressed the main study question and would have resulted in a loss of statistical power. L.R. If there is potential for other factors to influence associations, investigators should try to control these factors by design (eg, stratification) or be sure to measure them so that they might be controlled statistically using multivariable models, if the sample size allows for such models to be estimated. 4) Simpson’s Paradox When … Pitfalls of Ranking. In Poland people eat more than twice as much Sauerkraut per capita compared with Germans. And with more than 7 million members and more than 26,000 clubs, the German Football Federation (DFB) is the world’s largest individual sport association. Figure 3. 352 . The unit of analysis is the entity from which measurements of “n” are taken. Let’s start with the average size of a family at 1.3 persons. At the indicated time, cells are examined under a microscope, and cell protein is determined in the well using a calibrated grid. Cell protein over time by strain. For instance, on average each German person has less than two legs, exactly 1.99999. An overall test is performed first to assess whether differences are present among the responses defined by the factors of interest. Sample size determination is critical for every study design, whether animal studies, clinical trials, or longitudinal cohort studies. In designing even basic science experiments, investigators must pay careful attention to control groups (conditions), randomization, blinding, and replication. The value of replication is understood; however, replication is useful only if the repeated experiment is conducted under the same experimental conditions. Things become even more vague when using cell culture or assay mixtures, and researchers are not always consistent. Conversely, a comparison that fails to reach statistical significance is caused by either no true effect or a type II error. Local Info This design provides information on the effect of diet, the effect of genotype, and the combination of the 2. Here I list the most common pitfalls: The misuse of concepts that reflect the deadliness of SARS-CoV-2, which are the case fatality rate (CFR), the infection fatality rate (IFR), and the mortality rate (MR). Journal editors, and peer reviewers like to publish findings that are statistically significant, and surprising. When does the calculation of averages reach its limits as a method for describing complex issues? To deal with this problem of spurious AI-solutions, here we report a novel and automated algorithm using ideas from statistical mechanics. The basic assumptions for ANOVA are independence (ie, independent experimental units and not repeated assessments of the same unit), normally distributed outcomes, and homogeneity of variances across comparison groups. The authors write with authority, experience, and humor and makes for a very enjoyable and informative reading experience. Cat indicates catalase; SOD, superoxide dismutase; TG, transgenic; WT, wild type. We find that most basic science studies involve hypothesis testing. In developing competing treatments or experimental conditions, the various conditions should be identical in every way except for the experimental condition under study. Mean percentage of apoptosis can be compared among strains treated with control (Ad‐LacZ) using t tests comparing 2 groups or ANOVA comparing >2 groups, assuming that the percentage of apoptosis is approximately normally distributed (significant differences [P<0.05] are noted against wild type treated with Ad‐LacZ). If the outcome being compared among groups is continuous, then means and standard errors should be presented for each group. Chapter 5 Pitfalls to avoid. You are known for treating your subject with a healthy sense of humour. The chi‐square test (used with categorical and ordinal outcomes) also assumes independence and an expected count of at least 5 in each comparison group. Crime Statistics. When three friends drink an average of five beers in an evening, it makes a big difference whether they all drink the same amount, or one of them drinks 15 beers and the others none. Pairwise comparisons (2 at a time) are perhaps the most popular, but general contrasts (eg, comparing the mean of groups 1 and 2 with the mean of groups 3 and 4) are also possible with these procedures. The intervention consisted of eight home visits from specially trained community nurses in the first 24 months after birth. A particular challenge in sample size determination is estimating the variability of the outcome, particularly because different experimental designs require distinct approaches. Failure to explore the data. The Sauerkraut cliché is completely misleading. A common mistake is not considering the specific requirements to analyze matched or paired data. Figure 5. I told her not to worry because "Statistically, it's more likely that a person will die on the way to the hospital than during Basic science experiments often have many statistical comparisons of interest. This can be done with graphic displays or assessment of distributional properties of the outcome within the current study or reported elsewhere (note that the assumption of normality relates to normality of the outcome in the population and not in the current study sample alone). You would like to receive regular information about Germany? The unit of analysis is the isolate, and data are combined from each experiment (different days) and summarized as shown in Figure 6. A cluster randomised controlled trial study design was used. Because of the random, or as statisticians like to call it, “stochastic,” nature of conversion events, a test might not … If the statistical interaction is significant, then the interaction should be reported and formal tests for main effects should be omitted (because there are different associations depending on the second factor, as discussed in detail by Kleinbaum et al6). The goal is to ensure that bias (systematic errors introduced in the conduct, analysis, or interpretation of study results) and confounding (distortions of effect caused by other factors) are minimized to produce valid estimates of effect. One of the most common pitfalls in statistics is the misunderstanding that the data in hand are fully representative of the system being studied. Based on the usual parameters such as income, wealth, life expectancy, years of school education, or the number of children per family, people in Germany are refreshingly average in Europe. Let’s start with the average size of a family at 1.3 persons. The data are means and standard errors taken over n=6 isolates for each type of mouse and condition. The sample size, which affects the appropriate statistical approach used for formal testing, is the number (ie, n value) of independent observations under 1 experimental condition. Or when are other parameters, such as extremes, more meaningful? However, the VITAMINS trial in patients with septic shock adopted a composite of mortality and vasopressor‐free days, and an ordinal scale describing patient status rapidly became standard in COVID studies. This type of analysis accounts for the dependencies of observations measured repeatedly. Phys. A simple example is a single measurement (eg, weight) performed on 5 mice under the same condition (eg, before dietary manipulation), for n=5. Note that 1‐factor and higher order ANOVAs are also based on assumptions that must be met for their appropriate use (eg, normality or large samples). Each of these statistical tests assumes specific characteristics about the data for their appropriate use. In basic science research, studies are often designed with limited consideration of appropriate sample size. This makes sense from a business standpoint. If the calculated sample size is not practical, alternative outcome measures with reduced variability could be used to reduce sample size requirements. Although this approach is very easy to implement, it is overly conservative. 7272 Greenville Ave. A significant statistical finding (eg, P<0.05 when the significance criterion is set at 5%) is due to a true effect or a difference or to a type I error. In some experiments, it might be useful to display the actual observed measurements under each condition. e.Med Interdisziplinär. Figure 4. As a statistician, which figures and facts would you use to best describe the people in Germany? Professor Krämer, our topic is “Germany in general”. Many multiple comparison procedures exist, and most are available in standard statistical computing packages. Minimizing type II error and increasing statistical power are generally achieved with appropriately large sample sizes (calculated based on expected variability). An appropriate analytic technique is a repeated‐measures ANOVA with 1 between factor (ie, genotype) and 1 within factor (ie, time). The examples given are general guidelines. In this case people are far more interested in the extremes. In the above example, wild‐type and genetically altered littermates could be randomized in sufficient numbers to competing diets and observed for blood pressure, left ventricular mass, and serum biomarkers. Ordinal and categorical variables are best displayed with relative frequency histograms and bar charts, respectively (Figure 4). With large samples (n>30 per group), normality is typically ensured by the central limit theorem; however, with small sample sizes in many basic science experiments, normality must be specifically examined. ;5, Normality tests for statistical analysis: a guide for non‐statisticians, Strategies for dealing with multiple treatment comparisons in confirmatory clinical trials, Statistical primer for cardiovascular research: multiple comparisons procedures, Statistical primer for cardiovascular research: survival methods, Journal of the American Heart Association, Common Statistical Pitfalls in Basic Science Research, Creative Commons Attribution‐NonCommercial, Goal: Describe the distribution of observations measured in the study sample, Sample size (n) and relative frequency (%), Independence of observations, normality or large samples, and homogeneity of variances, Independence of pairs, normality or large samples, and homogeneity of variances, Repeated measures in independent observations, normality or large samples, and homogeneity of variances, Independence of observations, expected count >5 in each cell. Which often quoted figures used to describe people in Germany are quickly misleading? Most common statistical methods assume that each unit of analysis is an independent measurement. This approach can be appropriate, but with many statistical tests, investigators must recognize the possibility of a false‐positive result and, at a minimum, recognize this particular limitation. Ethical considerations elevate the need for sample size determination as a formal component of all research investigations. The outcome of interest is again normalized blood flow (a continuous outcome), and the comparison of interest is the trajectory (pattern over time) of mean normalized blood flow between strains. Data can be summarized as shown in Figure 7 and are displayed as means and standard error bars for each time point and compared statistically using repeated‐measures ANOVA (again, assuming that cell protein levels are approximately normally distributed). 1-800-AHA-USA-1 The unit of analysis is the entity from which measurements of “n” are taken. This article aims at raising awareness for a responsible handling of study data and for avoiding questionable or incorrect practices. A single measurement is taken for each mouse. If it is of interest to compare all pairs of experimental conditions, then the Tukey or Duncan test may be best, depending on the number of desired comparisons and the sample sizes. Such an approach not only fails to examine longitudinal effects contained in the data but also results in decreased statistical power compared with a repeated‐measures analysis. Basic science studies often involve several outcome variables from the same sample (eg, group of mice), making sample size decisions challenging. *P<0.05 against wild type treated with Ad‐LacZ. When summarizing continuous outcomes in each comparison group, means and standard errors should be used. We need to be alert to potential pitfalls. Determining what statistical technique or test to do when: (1) mean and standard deviation if no extreme or outlying values are present; (2) independence of observations, normality or large samples, and homogeneity of variances; (3) independence of pairs, normality or large samples, and homogeneity of variances; (4) repeated measures in independent observations, normality or large samples, and homogeneity of variances; (5) independence of observations and expected count >5 in each cell; (6) repeated measures in independent observations. This is an open access article under the terms of the. Confronting pitfalls of AI-augmented molecular dynamics using statistical physics J. Chem. Percentage of apoptosis by strain. Read preview. A single figure, such as the number of people employed by the big banks, is often not enough to understand how an entire industry is performing. Each time a statistical test is performed, it is possible that the statistical test will be significant by chance alone when, in fact, there is no effect (ie, a type I error). We aim to provide a non-technical and easily accessible resource for statistical practitioners who wish to spot and avoid misinterpretations and misuses of statistical significance tests. A common pitfall in basic science studies is a sample size that is too small to robustly detect or exclude meaningful effects, thereby compromising study conclusions. Philip Sedgwick reader in medical statistics and medical education. These issues and their implications are discussed next. There are also specific statistical tests of normality (eg, Kolmogorov‐Smirnov, Shapiro‐Wilk), but investigators should be aware that these tests are generally designed for large sample sizes.5 If one cannot assume normality, the most conservative strategy is to use a nonparametric test designed for nonnormal data. The misleading average, the graph 240. We have discussed issues related to sample size and power, study design, data analysis, and presentation of results (more details are provided by Katz2 and Rosner3). In this review, we focused on common sources of confusion and errors in the analysis and interpretation of basic science studies. Trading in a foreign country can be fraught with pitfalls. We wish to compare apoptosis in cell isolates in 3 different strains of mice (wild type and 2 strains of transgenic [TG] mice) treated with control (Ad‐LacZ) versus adenoviruses expressing catalase or superoxide dismutase. 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