Common Statistical Tests Research Articles

Conducting Power Analyses to Determine Sample Sizes in Quantitative Research: A Primer for Technology Education Researchers Using Common Statistical Tests

Conducting Power Analyses to Determine Sample Sizes in Quantitative Research: A Primer for Technology Education Researchers Using Common Statistical Tests

Key points of statistics for clinically oriented research

AbstractProcessing of research data is a crucial point in every scientific study. The basis of each statistical test is a clear research question and hypothesis. Then the scientist must consider which type of data are to be collected (nominal, ordinal, continuous). It is recommended that study-specific spreadsheets are developed before data collection, which consider the type of data being collected, how the data are to be analyzed, and which software will be used. Then, once the data have been gathered, tests for normality should be applied before proceeding with the specific statistical tests. This article provides a brief overview of common statistical tests based on fictional examples.

Assessing Evidence-Based Practice Knowledge, Self-Efficacy, and Use Among Respiratory Therapists.

Evidence-based practice is at the forefront of providing quality patient care by using the best available evidence and clinical expertise, while also considering patient needs and preferences for clinical decisions. However, evidence-based practice may not be consistently used even when the evidence supports the therapy. The purpose of this study was to assess the factors associated with the use of evidence-based practice among respiratory therapy faculty teaching in a large community college system and post-professional students enrolled in a university-based, respiratory therapy baccalaureate degree-advancement program. A non-probability, descriptive survey research design was used to develop and administer an online questionnaire. All respondents demonstrated sufficient knowledge and understanding of introductory concepts of evidence-based practice but knowledge of specific components of the evidence-based practice process was not as strong. Self-efficacy in knowledge and the use of evidence-based practice among faculty and degree-advancement students varied. Faculty and students rated their self-efficacy high in assessing patients' needs, values, and treatment preferences but ratings were lower for using the PICO (patient/population/problem, intervention, comparison, outcome) technique and interpreting common statistical tests. Students viewed their previous evidence-based practice learning experiences more favorably compared with faculty (P = .008). Faculty and students searched and read the research literature more often compared with critically appraising and using the research literature. Logistic regression analysis indicated no statistically significant relationship of knowledge, self-efficacy, and learning experiences to the use of evidence-based practice among respiratory therapy students, Χ 2 (4, N = 54) = 7.73; P = .10. Analysis of the results suggested that respiratory therapy faculty and students were knowledgeable and confident with regard to evidence-based practice but their use of evidence-based practice in clinical decisions was limited. Although the evidence-based practice knowledge, self-efficacy, and learning experiences had minimal influence on the use of evidence-based practice, the results of the study provide a foundation for future research.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

Solving probabilistic optimal power flow with renewable energy sources in distribution networks using fire hawk optimizer

The main contribution in this article is proposing a modern meta-heuristic algorithm for solving nonlinear, nonconvex, and complex OPF problems effectively in distribution networks, considering RES. The Fire Hawk Optimizer (FHO) algorithm is proposed in this work. The considered objective function (OF) is the combined minimization of generating cost, active power loss (Plosses), and load bus voltage deviation (VD). The proposed strategy is validated using the standard IEEE 69-bus benchmark. The obtained results are compared with various recent meta-heuristic techniques such as Capuchin Search Algorithm (CSA), Bear Smell Search Algorithm (BSSA), Black Widow Optimization (BWO) Algorithm, Walrus Optimization Algorithm (WaOA), and Prairie Dog Optimization (PDO) Algorithm. One-way ANOVA and the Tukey test, two common statistical tests, are used to compare algorithms effectively. The FHO shows the best and superior performance with minimum mean cost and standard deviation values of 51.2711 and 5.1131 respectively. This demonstrates that FHO results are more consistent and less diverse. The FHO shows a lower computational time of (25.3241 s) as compared with other strategies. The statistical results confirm the proposed approach's reliability, stability, and consistency. The proposed method provided reliable, high-quality results with faster computing times. Additionally, it has a significant convergence that makes it superior to solving OPF problems in distribution networks with variable RES and NRES.

Understanding biochemistry: basic aspects of statistics for life sciences.

If the biological world is one thing it is variable. As scientists we seek to measure, quantify and explain the causes of this variation. The approach we take to this is remarkably similar whether our research is exploring global temperature, blood pressure, cancer incidence or enzyme kinetics. This approach involves defining clear research questions and applying statistical methods to answer them robustly. This article will introduce a practical example that will be used throughout, specifically whether genetic variation can explain variation in coffee consumption. We assume little experience with statistics and walk through the statistical approach that biologists can use, firstly by describing our data with summary statistics and then by using statistical tests to help arrive at answers to our research question. A General Linear Model (GLM) approach will be used as this is what many common statistical tests are. We explore how to visualise and report results, while checking the assumptions of our analysis. The better we can understand and apply statistics to biological problems, the better we can communicate results and future research to others. The popular statistical programming language R will be used throughout.

Log Likelihood Ratios for Common Statistical Tests Using the likelihoodR Package

The **likelihoodR** package has been developed to allow users to obtain statistics according to the likelihood approach to statistical inference. Commonly used tests are available in the package, such as: *t* tests, ANOVA, correlation, regression and a range of categorical analyses. In addition, there is a sample size calculator for *t* tests, based upon the concepts of strength of evidence, and the probabilities of misleading and weak evidence.

Counternull sets in randomized experiments

Consider a statistical analysis for a randomized experiment that draws inferences based on hypothesis testing. In such settings, the plausibility of a null hypothesis is often examined using a p-value associated with a test statistic. In controlled experiments such as the ones that Bob conducts, Fisher-exact p-values are available and should be used to help evaluate results rather than the more commonly reported asymptotic p-values associated with common statistical tests (e.g., t-tests). Low p-value typically indicates some evidence against the null hypothesis, and when p-values are large, no definite statistical conclusions should be drawn! To avoid misinterpretations when testing a null hypothesis, we will present the &amp;#x22;counternull value”, which was first defined by Rosenthal and Rubin (1994) and illustrate these concepts using some of Bob&amp;#x27;s chamber experiments.

Conduct Common Statistical Tests Online.

Conduct Common Statistical Tests Online.

Estimating sample sizes for evidentialttests

The evidential approach uses likelihood ratios, typically the natural logarithm of the likelihood ratio representing the amount of evidence for one hypothesis versus another. One of the barriers to using the approach is the unavailability of sample size calculations for commonly used statistical tests. The t test is the most common statistical test used in scientific publications. This paper derives the equations necessary to calculate evidential probabilities and hence the required sample size for different types of t tests. Compared with the conventional Neyman-Pearson approach, the evidential approach requires larger sample sizes. This drawback is countered by the fact that users know the probability for obtaining misleading evidence (strong evidence that points to the wrong hypothesis). Even with small sample sizes, this is quite small (around 0.05) and decreases further with increasing sample sizes. The main challenge faced by the evidential researcher is of obtaining sufficiently strong evidence for or against one of the two specified hypotheses. Like the probability of a Type II error, this probability is large with a small sample size and decreases as the sample size increases. Sample size is estimated by achieving a low probability (e.g. <.1) for the combined probability of misleading and weak evidence.

Evaluation of statistical approaches for association testing in noisy drug screening data

BackgroundIdentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment.ResultsTo address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets.ConclusionsWe observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

Building Physician-Scientist Skills in R Programming:A Short Workshop Report.

Introduction:Statistical analysis programs require coding experience and a basic understanding of programming, skills which are not taught as part of medical school or residency curricula.Methods:We conducted a five-day course for early-career Nigerian physician-scientists interested in learning common statistical tests and acquiring R programming skills. The workshop included didactic presentations, small group learning activities, and interactive discussions. A baseline questionnaire captured participant demographics and solicited participants’ level of confidence in understanding/performing common statistical tests. REDCap questionnaires were emailed to obtain feedback on educational format and content. A post-workshop assessment covered participants’ overall impression of the program.Results:A total of 23 participants attended the program. Most participants were male (n=14, 60.9%) and at an early stage in their career (assistant professor, n=20, 87.0%). Approximately 70% of respondents indicated having received some prior training in statistics. The proportion of participants without experience using R and SAS software (90% and 85%, respectively) was greater than the corresponding proportions for Stata (55%) and SPSS (20%). Prior to the workshop, most respondents expressed being “not at all confident” in performing one-way ANOVA (60%), logistic regression (68%), simple linear regression (60%), and McNemar’s test (80%). There was a statistically significant post-workshop improvement in the level of confidence in understanding and performing common statistical tests. The course was rated on a 0–100 scale as “moderately difficult” (mean ± SD: 51.7 ± 19.5). Most participants felt comfortable in putting the knowledge learned into practice (82.2 ± 17.1).Conclusion and Public Health Implications:Introductory R can be taught to junior physician-scientists in resource-limited settings and can inform the development and implementation of similar training initiatives in analogous settings.

How to conduct inferential statistics online: A brief hands-on guide for biomedical researchers

Introduction: Research data are first organized and visualized with the help of descriptive statistics. The next step is the inferential statistics. Result of the inferential statistics helps to conclude the finding. Many researchers and medical students may not have access to dedicated software for biostatistics. Aim: This study aimed to provide a guide on the conduct of common inferential statistics that can be done online. Methods: Common inferential statistical tests for both numerical data and categorical data were described in this study. All the tests were conducted online and the process is described step by step with example data. Results: The following tests were described-one-sample t-test, one-sample median test, unpaired t-test, Mann–Whitney U-test, paired t-test, Wilcoxon signed-rank test, one-way analysis of variance (ANOVA), Kruskal–Wallis test, repeated-measure ANOVA, Friedman Test, Pearson correlation test, Spearman correlation test, Binomial test, Chi-square test, Fisher's exact test, and MacNemar test. All these tests could be conducted online from a computer connected to the internet. Conclusion: We could conduct common inferential statistical tests online without any installed software. Anyone without prior data analysis knowledge may conduct the tests with example data on any internet browser. We presume that these would help the medical undergraduate and postgraduate students.

How to conduct inferential statistics online (Part 2): A brief hands-on guide for biomedical researchers

Introduction: Researchers from developing countries may not have access to statistical software packages. However, descriptive and inferential statistical tests are to conduct to conclude the study. In a previous article (DOI: 10.4103/ijves.ijves_116_21), we described how to conduct some of the common inferential statistical tests online. This article is the second part of the series. Aim: We aimed to provide the examples of some inferential statistical tests used in clinical studies and provide step-by-step guidelines to conduct those tests online. Methods: We prepared a set of data for each statistical test. These data were used to carry out the test online and the steps are briefly described. The result of the test is presented with screenshots and text to get an idea of how to report the result in a manuscript. Results: We described the process of conduct of the following tests online – Receiver operating characteristics curve analysis, Kaplan − Meier estimate, dose-response, logistic regression, multiple linear regression, residual analysis, odd ratio, Bland − Altman plot, Cronbach's alpha, Cohen's kappa, and intraclass correlation coefficient. In addition, a method for random allocation of subjects in groups was also described. All the tests were described with example data available in a supplementary file. Conclusion: In this article, some of the inferential statistics used for clinical studies are described with example data and a step-by-step guide. Any clinician from resource-limited settings may use this guide as a reference for statistical tests. However, the tests described in this article are not a comprehensive list.

How to Conduct Descriptive Statistics Online

Background: Descriptive statistics is the first step of data analysis. In biomedical researches, inferential statistical tests are invariably conducted after descriptive statistical tests for getting a summary of the data. Many resource-limited settings may not have dedicated software for carrying out these tests. Aim: This article aimed to provide a brief technical guide about the conduct of descriptive statistics with visualization that can be done without any dedicated statistical software package. Methods: We searched for online tools that provide free service for the conduct of descriptive statistics. The example data were fabricated for the conduct of the test online. The visualization of the data (i.e., figures) was explained in brief, wherever necessary. Results: We described the method to graph and summarize the data using a pie chart, frequency table, stem and leaf display, histogram, frequency polygon, box plot, bar chart, stacked bar chart, line graph, dot plot, central tendency, variance, quantile-quantile plot, scatter plot, and Venn diagram. All these tests and visualization were done online without any installed dedicated software package. Conclusion: This article provides a brief technical guide for conducting common descriptive statistical tests online. Researchers in any resource-limited settings may use these services to summarize and visualize the data online from public domain websites.

Statistical Assumptions in Orthopaedic Literature: Are Study Findings at Risk?

BackgroundAs orthopaedic surgery becomes more evidence-based, the need for rigorous research has increased. This results in more complex studies that employ more sophisticated statistical analysis, often some form of regression. These statistical techniques require the data to meet certain assumptions for the findings to be considered valid. The purpose of this study is to determine the common regression techniques employed in the orthopaedic surgery literature, and demonstrate how often the assumptions of regression analyses are met and reported.MethodsStudies published in the Journal of Bone & Joint Surgery (JBJS) in 2017 and 2018 were reviewed. Commentaries, editorials, and systematic reviews were excluded. The statistical analyses performed in each study were documented. When regression analyses were utilized, the article was reviewed for evidence that the necessary assumptions underlying the statistical methodology were assessed and met.ResultsFrom the 470 studies that were reviewed, the most common statistical test reported was the independent-samples t-test (n=215, 45.7%). Also, 201 studies (42.8%) implemented some form of regression analysis. The most common regression was a logistic regression (n= 106). None of the 201 studies using regression analysis reported meeting all of the necessary assumptions to appropriately use a regression test.ConclusionMany recent studies published in JBJS depended on regression analyses to reach their conclusions, but none fully reported the necessary assumptions of these tests. Orthopaedic surgery journals should be more transparent in reporting the methodology of statistical tests, and readers must beware of possible gaps in statistical methodology and critically evaluate the studies' findings.

The Importance of Null Hypotheses: Understanding Differences in Local Moran’s under Heteroskedasticity

A recent review noted important differences in the results of the local Moran's statistic depending on the inference method. These differences had significant practical implications. In closing, the authors speculated the differences may be due to local spatial heterogeneity. In this article, we propose that different null hypotheses, not heteroskedasticity, generate these differences. To test this, we examine the null hypotheses implicit in common statistical significance tests of local Moran’s . We design an experiment to assess the impact of local heterogeneity on tests conducted under the two most common null hypotheses. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran’s statistic. We run this experiment with controlled synthetic heteroskedastic data and with uncontrolled real‐world data with varying degrees and patterns of local heteroskedasticity. We show that, in both situations, estimates that use the same null are extremely similar, regardless of estimation method. In contrast, all estimates (regardless of the null) are moderately affected by spatial heteroskedasticity. Ultimately, this article demonstrates that there are important conceptual and computational differences about null hypothesis in local testing frameworks, and these differences can have significant practical implications. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns.

A Guide to Basic Statistics for Educational Research.

IntroductionClinician-educators often need to produce scholarship for academic promotion. While some programs exist to help with faculty development skills, few provide adequate statistical training to help educators evaluate their work.MethodsFrom January 2020 through January 2021, faculty at three academic centers attended one of five in-person or virtual seminars with dedicated statistical training for medical education interventions. These 90-minute seminars included a 45-minute PowerPoint presentation of common statistical tests used for educational interventions followed by small breakout groups to help attendees work on additional practice examples. After each seminar, surveys were distributed in person or virtually to obtain feedback.ResultsForty-three faculty attended the five seminars, with a range of surgical and nonsurgical specialties represented. Of these attendees, 38 (88%) completed session evaluations. The majority of respondents (n = 34, 90%) rated the session as extremely useful in helping them know how to use statistics in their scholarly work. Most participants agreed or strongly agreed they had adequate time to practice skills (n = 30, 79%). Self-rated confidence in using statistics was significantly higher after the session compared to before (3.00 post vs. 1.97 pre, p < .0001). Most participants (n = 32, 84%) rated the session as excellent and the small-group practice as most useful (n = 16, 42%), but many (n = 26, 69%) wanted more skills practice.DiscussionThis intervention shows that dedicated training on biostatistics used in educational interventions can help clinician-educators improve self-rated confidence and knowledge in choosing statistical tests in educational scholarship.

Scaling Analysis of an Image Encryption Scheme Based on Chaotic Dynamical Systems.

Despite that many image encryption systems based on chaotic or hyperchaotic systems have been proposed to protect different kinds of information, it has been crucial to achieve as much security as possible in such systems. In this sense, we numerically implement a known image encryption system with some variants, making special emphasis when two operations are considered in the scrambling stage. The variants of such an encryption system are based on some hyperchaotic systems, which generated some substitution boxes and the keys of the system. With the aim to have a more complete evaluation, some internal stages of the image encryption scheme have been evaluated by using common statistical tests, and also the scaling behavior of the encrypted images has been calculated by means of a two-dimensional detrended fluctuation analysis (2D-DFA). Our results show that the image encryption systems that include two operations or transformations in the scrambling stage present a better performance than those encryption systems that consider just one operation. In fact, the 2D-DFA approach was more sensitive than some common statistical tests to determine more clearly the impact of multiple operations in the scrambling process, confirming that this scaling method can be used as a perceptual security metric, and it may contribute to having better image encryption systems.

Testing for differences between two distributions in the presence of serial correlation using theKolmogorov–Smirnovand Kuiper's tests

AbstractTesting for differences between two states is a staple of climate research, for example, applying a Student'sttest to test for the differences in means. A more general approach is to test for differences in the entire distributions. Increasingly, this latter approach is being used in the context of climate change research where some societal impacts may be more sensitive to changes further from the centre of the distribution. The Kolmogorov–Smirnov (KS) test, probably the most widely‐used method in distributional testing, along with the closely related, but lesser known Kuiper's (KU) test are examined here. These, like most common statistical tests, assume that the data to which they are applied consist of independent observations. Unfortunately, commonly used data such as daily time series of temperature typically violate this assumption due to day‐to‐day autocorrelation. This work explores the consequences of this. Three variants of the KS and KU tests are explored: the traditional approach ignoring autocorrelation, use of an ‘effective sample size’ based on the lag‐1 autocorrelation, and Monte Carlo simulations employing a first order autoregressive model appropriate to a variety of data commonly used in climate science. Results indicate that large errors in inferences are possible when the temporal coherence is ignored. The guidance and materials provided here can be used to anticipate the magnitude of the errors. Bias caused by the errors can be mitigated via easy to use ‘look‐up’ tables or more broadly through application of polynomial coefficients fit to the simulation results.