Insights from Data with R

2021 ◽  
Author(s):  
Owen L. Petchey ◽  
Andrew P. Beckerman ◽  
Natalie Cooper ◽  
Dylan Z. Childs
Keyword(s):  
Statistical Tests ◽  
Effect Sizes ◽  
Sample Population ◽  
Raw Data ◽  
P Values ◽  
Undergraduate Level ◽  
Key Concepts ◽  
Solid Foundation ◽  
Second Year ◽  
Data Summaries

Knowledge of how to get useful information from data is essential in the life and environmental sciences. This book provides learners with knowledge, experience, and confidence about how to efficiently and reliably discover useful information from data. The content is developed from first- and second-year undergraduate-level courses taught by the authors. It charts the journey from question, to raw data, to clean and tidy data, to visualizations that provide insights. This journey is presented as a repeatable workflow fit for use with many types of question, study, and data. Readers discover how to use R and RStudio, and learn key concepts for drawing appropriate conclusions from patterns in data. The book focuses on providing learners with a solid foundation of skills for working with data, and for getting useful information from data summaries and visualizations. It focuses on the strength of patterns (i.e. effect sizes) and their meaning (e.g. correlation or causation). It purposefully stays away from statistical tests and p-values. Concepts covered include distribution, sample, population, mean, median, mode, variance, standard deviation, correlation, interactions, and non-independence. The journey from data to insight is illustrated by one workflow demonstration in the book, and three online. Each involves data collected in a real study. Readers can follow along by downloading the data, and learning from the descriptions of each step in the journey from the raw data to visualizations that show the answers to the questions posed in the original studies.

Assessing variability in surgical decision making among attending neurosurgeons at an academic center

2020 ◽  
Vol 132 (6) ◽  
pp. 1970-1976
Author(s):  
Ashwin G. Ramayya ◽  
H. Isaac Chen ◽  
Paul J. Marcotte ◽  
Steven Brem ◽  
Eric L. Zager ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Repeated Measures ◽  
Elective Surgery ◽  
Statistical Tests ◽  
Neurosurgical Procedures ◽  
P Values ◽  
Surgical Interventions ◽  
Radiology Reports ◽  
Operative Notes ◽  
First Time

OBJECTIVEAlthough it is known that intersurgeon variability in offering elective surgery can have major consequences for patient morbidity and healthcare spending, data addressing variability within neurosurgery are scarce. The authors performed a prospective peer review study of randomly selected neurosurgery cases in order to assess the extent of consensus regarding the decision to offer elective surgery among attending neurosurgeons across one large academic institution.METHODSAll consecutive patients who had undergone standard inpatient surgical interventions of 1 of 4 types (craniotomy for tumor [CFT], nonacute redo CFT, first-time spine surgery with/without instrumentation, and nonacute redo spine surgery with/without instrumentation) during the period 2015–2017 were retrospectively enrolled (n = 9156 patient surgeries, n = 80 randomly selected individual cases, n = 20 index cases of each type randomly selected for review). The selected cases were scored by attending neurosurgeons using a need for surgery (NFS) score based on clinical data (patient demographics, preoperative notes, radiology reports, and operative notes; n = 616 independent case reviews). Attending neurosurgeon reviewers were blinded as to performing provider and surgical outcome. Aggregate NFS scores across various categories were measured. The authors employed a repeated-measures mixed ANOVA model with autoregressive variance structure to compute omnibus statistical tests across the various surgery types. Interrater reliability (IRR) was measured using Cohen’s kappa based on binary NFS scores.RESULTSOverall, the authors found that most of the neurosurgical procedures studied were rated as “indicated” by blinded attending neurosurgeons (mean NFS = 88.3, all p values < 0.001) with greater agreement among neurosurgeon raters than expected by chance (IRR = 81.78%, p = 0.016). Redo surgery had lower NFS scores and IRR scores than first-time surgery, both for craniotomy and spine surgery (ANOVA, all p values < 0.01). Spine surgeries with fusion had lower NFS scores than spine surgeries without fusion procedures (p < 0.01).CONCLUSIONSThere was general agreement among neurosurgeons in terms of indication for surgery; however, revision surgery of all types and spine surgery with fusion procedures had the lowest amount of decision consensus. These results should guide efforts aimed at reducing unnecessary variability in surgical practice with the goal of effective allocation of healthcare resources to advance the value paradigm in neurosurgery.

scholarly journals Statistics Notes: Interaction 2: compare effect sizes not P values

BMJ ◽  
1996 ◽  
Vol 313 (7060) ◽  
pp. 808-808 ◽  
Author(s):  
J. N S Matthews ◽  
D. G Altman
Keyword(s):  
Effect Sizes ◽  
P Values

Crossword Puzzles: Active Learning in Undergraduate Pathology and Medical Education

2009 ◽  
Vol 133 (9) ◽  
pp. 1457-1462
Author(s):  
Anurag Saxena ◽  
Raenelle Nesbitt ◽  
Punam Pahwa ◽  
Sheryl Mills
Keyword(s):  
Student Perceptions ◽  
Small Groups ◽  
Time Frame ◽  
Teaching Tool ◽  
Internet Resource ◽  
Cooperative Groups ◽  
Key Concepts ◽  
Second Year ◽  
Peer Teachers ◽  
Short Time

Abstract Context.—Second-year medical students are introduced to many new terms and concepts in a short time frame in the hematology system and the neoplasia section of the undergraduate pathology course. It is a challenge to provide adequate practice and necessary repetition to reinforce key concepts. Objective.—To determine student perceptions of the usefulness of crosswords as a quick and effective way to reinforce essential concepts and vocabulary. Design.—Crosswords with ensured content validity built on a free Internet resource were completed by the students in collaborative and cooperative groups of 6 to 7 with a reward for the first group to successfully complete the puzzle. Student perceptions of the value of crosswords for their learning were examined in 2003 (39 students) with a survey of yes or no responses and in 2004 (41 students) with a survey using questions with a 5-point Likert scale. Results.—Many students (37 of 39 in 2003 and 24 of 41 in 2004) indicated that crosswords were useful and contributed to their learning. Specifically, crosswords were found to be useful for identifying key concepts and vocabulary and for their collaborative and competitive aspects. Written and informal comments indicated student enthusiasm for and a desire to participate in more of these exercises. Students have transferred this review strategy to other classes and the peer teachers have expressed an interest in it as an adjunct teaching tool. Conclusions.—The judicious use of crosswords was useful for near transfer content and provided an opportunity to discuss and recall essential concepts, think critically, and collaborate in small groups.

Make sense of the research: A primer for educational leaders

2021 ◽  
Vol 103 (3) ◽  
pp. 43-47
Author(s):  
David Steiner
Keyword(s):  
Educational Leaders ◽  
Decision Makers ◽  
Effect Sizes ◽  
Randomized Control Trials ◽  
P Values ◽  
Know How ◽  
High Effect ◽  
Education Leaders ◽  
Randomized Control ◽  
Education Decision

Education leaders know that they should use research when choosing interventions for their schools, but they don’t always know how to read the research that is available. David Steiner explains some of the reasons that reading research is a low priority for educators on the front lines and offers some guidance for determining whether research results are meaningful without an extensive background in statistics. Ideally, education decision makers should look for randomized control trials with high effect sizes and low p-values.

scholarly journals The earth is flat (p>0.05): Significance thresholds and the crisis of unreplicable research

2017 ◽  
Author(s):  
Valentin Amrhein ◽  
Fränzi Korner-Nievergelt ◽  
Tobias Roth
Keyword(s):  
Publication Bias ◽  
Null Hypothesis ◽  
Statistical Power ◽  
Alternative Hypothesis ◽  
Statistical Significance ◽  
Practical Importance ◽  
Decision Rules ◽  
Effect Sizes ◽  
P Values ◽  
True Effect

The widespread use of 'statistical significance' as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (American Statistical Association, Wasserstein & Lazar 2016). We review why degrading p-values into 'significant' and 'nonsignificant' contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p-values at face value, but mistrust results with larger p-values. In either case, p-values can tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance (p≤0.05) is hardly replicable: at a realistic statistical power of 40%, given that there is a true effect, only one in six studies will significantly replicate the significant result of another study. Even at a good power of 80%, results from two studies will be conflicting, in terms of significance, in one third of the cases if there is a true effect. This means that a replication cannot be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgement based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to publication bias against nonsignificant findings. Data dredging, p-hacking and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p-values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p-values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that 'there is no effect'. Information on possible true effect sizes that are compatible with the data must be obtained from the observed effect size, e.g., from a sample average, and from a measure of uncertainty, such as a confidence interval. We review how confusion about interpretation of larger p-values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, such as 'we need more stringent decision rules', 'sample sizes will decrease' or 'we need to get rid of p-values'.

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