Confronting pitfalls of AI-augmented molecular dynamics using statistical physics J. Chem. She avoids the pitfall of sensationalism. In basic science research, there is often no prior study, or great uncertainty exists regarding the expected variability of the outcome measure, making sample size calculations a challenge. We can consider three broad classes of statistical pitfalls. 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. In this instance, an efficient approach is to perform sample size computations for each outcome, and the largest practical sample size could be used for the entire experiment. Connor In basic science research, confounding due to other factors might be an issue; carefully designed experiments can minimize confounding. It is important to recognize that the lack of significance may be due to low statistical power. Pitfalls of statistical hypothesis testing: type I and type. The authors write with authority, experience, and humor and makes for a very enjoyable and informative reading experience." The first involves sources of bias. The Pitfalls of Statistics . Survival analyses can be particularly challenging for investigators in basic science research because small samples may not result in sufficient numbers of events (eg, deaths) to perform meaningful analysis. Unauthorized It is difficult to overestimate the value of plotting data. Blood flow over time by strain. Readers are going to be most interested in studies that uncover interesting, and new non-zero relationships. 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). Table 2 outlines some common statistical procedures used for different kinds of outcomes (eg, continuous, categorical) to make comparisons among competing experimental conditions with varying assumptions and alternatives. When summarizing continuous outcomes in each comparison group, means and standard errors should be used. In such a case, the observed effects can be used to design a larger study with greater power. In this review, we focused on common sources of confusion and errors in the analysis and interpretation of basic science studies. Failure to satisfy these assumed characteristics can lead to incorrect inferences and is a common oversight in basic science studies. They provide a basis for judgement but not the whole judgment.” —Prof. The unit of analysis is the entity from which measurements of “n” are taken. The examples given are general guidelines. Figure 4. The aim of the intervention was to improve the health and wellbeing of parents and children. For example: I had a friend who had a brain tumor and had to have surgery to remove it. This latter observation would escape detection if performed in separate experiments, and the factorial design has the advantage of involving fewer mice than would be required for the 2 separate experiments. 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. Local Info For continuous outcomes, means and standard errors should be provided for each condition (Figure 2). An appropriate analytic technique is a repeated‐measures ANOVA with 1 between factor (ie, genotype) and 1 within factor (ie, time). A randomised controlled superiority trial was used. Pitfalls of Ranking. L.R. In Poland people eat more than twice as much Sauerkraut per capita compared with Germans. Without Abstract. For instance, on average each German person has less than two legs, exactly 1.99999. Several statistical comparisons are of interest. 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. With large samples, randomization ensures that any unintentional bias and confounding are equally present in control and experimental groups. The misleading average, the graph 240. Germans move home far less often than people in other countries, such as in the USA. A single measurement is taken for each mouse. Careful specification of the experimental design will greatly aid investigators in calculating sample size. A typical “reasonable” value is ≥80% power. It is more appropriate to clearly indicate the exact sample size in each comparison group. In the case of averages it’s always important to keep the deviations in mind. Six isolates were taken from each strain of mice and plated into cell culture dishes, grown to confluence, and then treated as indicated on 6 different occasions. The unit of analysis is the entity from which measurements of “n” are taken. Naturally, she was nervous. Phys. Oct-Dec 2015;6(4):222-4. doi: 10.4103/2229-3485.167092. 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. Replication provides additional information to estimate desired effects and, perhaps more important, to quantify uncertainty in observed estimates (as outlined). In this case people are far more interested in the extremes. The promises and pitfalls of Benford's law. At the indicated time, cells are examined under a microscope, and cell protein is determined in the well using a calibrated grid. View credits, reviews, tracks and shop for the 1979 Vinyl release of Pitfalls Of The Ballroom on Discogs. If the sample size is relatively small (eg, n<20), then dot plots of the observed measurements are very useful (Figure 1). Minimizing type II error and increasing statistical power are generally achieved with appropriately large sample sizes (calculated based on expected variability). Investigators should evaluate the various procedures available and choose the one that best fits the goals of their study. 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)? Investigators should always perform sample size computations, particularly for experiments in which mortality is the outcome of interest, to ensure that sufficient numbers of experimental units are considered to produce meaningful results. 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). †P<0.05 between treated TG1 mice and TG1 treated with Ad‐LacZ. Such a manuscript structure is a challenge for analysis and statistical review. *P<0.05. Now let’s define two different zoning schemes: one which follows a uniform grid pattern and another that does not. If we measure the weight 12 times in 1 day, we have 12 measurements per mouse but still only 5 mice; therefore, we would still have n=5 but with 12 repeated measures rather than an n value of 5×12=60. One must understand if the experimental units assigned to comparison groups are independent (eg, only 1 treatment per unit) or repeated measurements taken on the same set of experimental units under differing conditions. Pitfalls in Statistics. Berlin is Germany’s largest city, but it doesn’t score all the top ratings. Department of Biostatistics, Boston University School of Public Health, Boston, MA, Division of Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA. Indeed, statistics is perhaps more open to misuse than any other subject, particularly by the nonspecialist. One of the greatest pitfalls of statistics is that the average person does not understand them AT ALL!!! Were this true we would be able to infer arbitrarily precise insights about that system as we collected more and more data. In basic science research, studies are often designed with limited consideration of appropriate sample size. On 7 different occasions, the cells are thawed and grown into the plates, and the experiments are performed. It might be that the effect of diet and genotype is additive, or there may be a statistical interaction (a different effect of diet on blood pressure depending on genotype). Journal editors, and peer reviewers like to publish findings that are statistically significant, and surprising. 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. Outcomes observed under each of the 4 conditions could be represented by means (for continuous variables) or proportions (for binary variables) and typically would be compared statistically with ANOVA or a chi‐square test, respectively. Cell protein over time by strain. 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). 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. This includes control of conditions that may unknowingly have an impact on the effects of the treatments under study (eg, time of day, temperature). Investigators can also minimize variability by carefully planning how many treatments, experimental conditions, or factors can be measured in an individual unit (eg, animal). This description includes the sample size (experimental n value) and appropriate numerical and graphical summaries of the data. Dallas, TX 75231 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). A type II error is described as a false‐negative result and occurs when the test fails to detect an effect that actually exists. Consider a study with 3 different experimental groups (eg, animal genotypes) with outcomes measured at 4 different time points. Chapter 5 Pitfalls to avoid. You are known for treating your subject with a healthy sense of humour. 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. Many statistical pitfalls lie in wait for the un-wary. 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. Sample sizes are often quite small and are not likely to support formal statistical testing of the underlying hypothesis. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. Consequently, there are multiple reasons why the statistical analysis of basic science research might be suboptimal. This design provides information on the effect of diet, the effect of genotype, and the combination of the 2. Or when are other parameters, such as extremes, more meaningful? 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. Let’s start with the average size of a family at 1.3 persons. e.Med Interdisziplinär. Because of the random, or as statisticians like to call it, “stochastic,” nature of conversion events, a test might not … Contact Us. A cluster randomised controlled trial study design was used. 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. In basic science research, investigators often have small sample sizes, and some of their statistical comparisons may fail to reach statistical significance. When the effects of >1 experimental condition are of interest, higher order or factorial ANOVA may be appropriate. William Goodman. We wish to compare organ blood flow recovery over time after arterial occlusion in 2 different strains of mice. In contrast, basic science studies are often handled less uniformly, perhaps because of the unique challenges inherent in this type of investigation. The probability of type II error is related to sample size and is most often described in terms of statistical power (power=1‐type II error probability) as the probability of rejecting a false‐null hypothesis. The units could be animals, organs, cells, or experimental mixtures (eg, enzyme assays, decay curves). This technique provides for randomization of treatment and control groups equally across potential sources of bias and confounding, such as time of day; stratification by morning or afternoon time slots would prevent any impact by time of day. Stratification is a means to combat bias and confounding. Similar tests can be conducted for TG mice (significant differences [P<0.05] are noted between treated TG1 mice and TG1 treated with Ad‐LacZ and between treated TG2 mice and TG2 treated with Ad‐LacZ). Numerous pitfalls await unsuspecting investors. Percentage of apoptosis by strain. Let’s define a 5km x 5km area and map the location of each individual inside the study area. Basic science studies often involve several outcome variables from the same sample (eg, group of mice), making sample size decisions challenging. A critically important first step in any data analysis is a careful description of the data. © 2016 The Authors. They are common and particularly difficult to catch for people whose main task isn’t statistics because they don’t see abnormalities. This shows that the banks’ value chain is increasingly distributed across supplier industries and also that statistics have their pitfalls. Time‐to‐event data have their own special features and need specialized statistical approaches to describe and compare groups in terms of their survival probabilities. Here are 15 places with outstanding characteristics. This is an open access article under the terms of the. Jetzt einloggen Kostenlos registrieren ★ PREMIUM-INHALT. If the outcome being compared among groups is continuous, then means and standard errors should be presented for each group. Several options exist for investigators to informatively display data in graphical format. You can find the answers here. The intervention consisted of eight home visits from specially trained community nurses in the first 24 months after birth. A common mistake is not considering the specific requirements to analyze matched or paired data. By Sherman, Alfred. Subscribe here: Statistics professor Walter Krämer, Technical University Dortmund. 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. Exceptions are their love of cars, their love of their homeland and their enthusiasm for football. An important consideration in determining the appropriate statistical test is the relationship, if any, among the experimental units in the comparison groups. The former reflects the inherent biological variability, whereas the latter may simply measure assay variability. Development of heart failure (%) by type. If the latter condition is not satisfied, an alternative exact test (eg, Fisher's exact test) should be used. When does the calculation of averages reach its limits as a method for describing complex issues? The third class of problems concerns interpretation of results, or how statistical results are applied (or misapplied) to real world … And sometimes averages are totally uninteresting. Common Statistical Pitfalls in Setting Up an Analysis 1. Comparisons between experimental conditions in terms of survival are often performed with the log‐rank test. Ethical considerations elevate the need for sample size determination as a formal component of all research investigations. One of the most common pitfalls in statistics is the misunderstanding that the data in hand are fully representative of the system being studied. This makes sense from a business standpoint. Mean and standard error of systolic blood pressure (SBP) by type. The unit of analysis is the mouse, and we have repeated measurements of blood flow (before occlusion, at the time of occlusion [time 0], and then at 1, 3, 7, 14, 21, and 28 days). If the calculated sample size is not practical, alternative outcome measures with reduced variability could be used to reduce sample size requirements. Let’s assume, for sake of argument, that individuals are laid out in a perfect grid pattern. 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. Ordinal and categorical variables are best displayed with relative frequency histograms and bar charts, respectively (Figure 4). The outcome of interest is percentage of apoptosis (a continuous outcome), and the comparison of interest is percentage of apoptosis among strains. The 9 Pitfalls of Data Science is the modern version of the classic book, How to Lie with Statistics. Professor at the University of Ontario Institute of Technology, where he teaches business statistics, forecasting and risk management. Most common statistical methods assume that each unit of analysis is an independent measurement. When determining the requisite number of experimental units, investigators should specify a primary outcome variable and whether the goal is hypothesis testing (eg, a statistical hypothesis test to produce an exact statistical significance level, called a P value) or estimation (eg, by use of a confidence interval). However, only 13,710 deaths have been recorded as COVID-19-related over the same period, which explains only 54% of the observed excess mortality. And the Sauerkraut cliché is completely misleading. 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. This clearly illustrates that the normal use of arithmetic averages results in values that simply don’t occur in real life. Hassloch in Rhineland-Palatinate is regarded as the quintessential average community in Germany. The second category is errors in methodology, which can lead to inaccurate or invalid results. These issues and their implications are discussed next. 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 1-800-242-8721 The habit of presenting sample sizes as ranges (eg, n=5 to 12 in each group) is not useful from a statistical perspective. An important implication of appropriate sample determination is minimizing known types of statistical errors. Multiple comparison procedures are techniques that allow for more comparisons but that control the overall type I error rate for the set of all comparisons. PUBLIC SPENDING by Evan Davis . 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. Penguin, in association with the Social Market Foundation, f8.99, pp. If the outcome were not approximately normally distributed, then a nonparametric alternative such as the Wilcoxon rank sum or Mann–Whitney U test could be used instead. The unit of analysis is the isolate, and we have repeated measurements of cell protein at baseline (time 0) and then at 1, 3, 5, 7, and 9 hours. 352 . 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). Investigators can limit type I error by making conservative estimates such that sample sizes support even more stringent significance criteria (eg, 1%). In many settings, multiple statistical approaches are appropriate. 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. 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. In every study, it is important to recognize limitations. Sample size determination is critical for every study design, whether animal studies, clinical trials, or longitudinal cohort studies. The units could be animals, organs, cells, or experimental mixtures (eg, enzyme assays, decay curves). 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. Data can be summarized as shown in Figure 5, in which means and standard error bars are shown for each time point and compared statistically using repeated‐measures ANOVA (again, assuming that normalized blood flow is approximately normally distributed). 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. As a statistician, which figures and facts would you use to best describe the people in Germany? Foremost, only those statistical comparisons that are of scientific interest should be conducted. The unit of analysis is the isolate, and data are combined from each experiment (different days) and summarized as shown in Figure 6. *P<0.05. 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. 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. 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. Most common statistical methods assume that each unit of analysis is an independent measurement. Concurrent control groups are preferred over historical controls, and littermates make the best controls for genetically altered mice. Another alternative is to transform the data (by log or square root) to yield a normal distribution and then to perform analyses on the transformed data. Figure 6. Data can be summarized as shown in Table 3 and compared statistically using the unpaired t test (assuming that normalized blood flow is approximately normally distributed). Unfortunately, these different concepts are sometimes used interchangeably. By convention, an independent experiment infers that the researcher has independently set up identical experiments each time rather than just measuring the outcome multiple times. In the absence of statistical interaction, one is free to test for the main effects of each factor. Summarizing evidence and drawing conclusions based on the data are particularly challenging because of the complexity of study designs, small sample sizes, and novel outcome measures. Careful attention to the research question, outcomes of interest, relevant comparisons (experimental condition versus an appropriate control), and unit of analysis (to determine sample size) is critical for determining appropriate statistical tests to support precise inferences. 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