Data science and statistics research

Field at a glance

Research Methods & Statistics

• ARIMA Models for Research Time Series: AR, I, MA, and Box-Jenkins Identification

  A researcher-facing guide to ARIMA: what the AR, I, and MA components each correct for, how to test stationarity with ADF and KPSS before fitting, and…

• Artificial Intelligence vs Data Science: What Is the Difference?Comparison

  AI builds systems that perceive, reason, learn and act; data science extracts insight from data. They overlap in machine learning but differ in goals,…

• Autocorrelation in Time-Series Data: ACF, PACF, and What It Breaks

  What autocorrelation is, why it violates the independence assumption, and how to read the ACF and PACF correlograms used to diagnose it — the concept…

• Average Variance Extracted (AVE): Calculation, the 0.50 Threshold, and Discriminant Validity

  AVE is the mean proportion of indicator variance a construct explains rather than error. This guide gives the calculation, the origin and conventional…

• Bayesian Hierarchical Models: Partial Pooling Explained (With a Worked Example)

  Hierarchical (multilevel) Bayesian models use partial pooling to shrink each group's estimate toward the overall mean by an amount set by its own samp…

• Bayesian Sample Size Determination

  Three genuinely different Bayesian answers to how much data is enough: precision-based estimation, assurance (power averaged over the prior), and sequ…

• Bayesian vs. Frequentist Statistics: What the Interval Actually MeansComparison

  A frequentist confidence interval and a Bayesian credible interval can be numerically close and still answer different questions -- worked out here wi…

• Biostatistics vs Epidemiology: What Is the Difference?Comparison

  Epidemiology studies how disease is distributed in populations and what determines it; biostatistics develops and applies the statistical methods that…

• Bootstrapping in Statistics: Resampling for Confidence Intervals

  Bootstrapping estimates a statistic’s uncertainty by resampling your own data with replacement thousands of times. This guide walks the procedure on a…

• Cluster Sampling: Definition, Design Effect, and When to Use It

  How cluster sampling works, why it trades precision for cost and feasibility, one/two/multistage designs, sampling weight construction, the design eff…

• Computer Science vs Data Science: Key DifferencesComparison

  Computer science studies computation, algorithms and systems; data science extracts knowledge from data. Compared on methods, math, training, funding…

• Cook's Distance and Influential Observations: Competing Thresholds, Leverage vs DFBETAS, and What to Do Next

  The three Cook's distance thresholds in circulation disagree with each other and none is what Cook proposed. A worked R demonstration separating outli…

• Cronbach's Alpha: What It Measures, How to Interpret It, and When to Use Omega Instead

  Cronbach's alpha measures internal consistency, not unidimensionality or reliability in general — the most common misreading of the statistic. This gu…

• DAGitty: Guide to Causal DAGs & Adjustment Sets

  A guide to DAGitty for causal inference: learn to map directed acyclic graphs, derive minimal sufficient adjustment sets, and prevent confounding bias…

• Data Science vs Statistics: What Is the Difference?Comparison

  Statistics develops the theory of inference and study design; data science adds computation at scale, data engineering and domain work. They share an…

• Difference-in-Differences: Evidencing Parallel Trends and the Staggered-Adoption Problem

  Parallel trends is a counterfactual assumption, not something a pre-trend plot can confirm. This guide sets out what each diagnostic actually establis…

• Directed Acyclic Graphs for Confounder Selection: Finding the Minimal Adjustment Set

  How to turn a directed acyclic graph into an operational adjustment-set procedure (the backdoor criterion) instead of a diagramming convention — inclu…

• Durbin-Watson Test for Autocorrelated Residuals: Reading the Statistic and the Inconclusive Zone

  Autocorrelated residuals don't bias OLS coefficients — they invalidate the standard errors. This guide covers reading the Durbin-Watson statistic's 0–…

• Endogeneity: The Three Sources, and the Remedy That Matches Each

  Endogeneity is three different problems - omitted-variable bias, simultaneity and measurement error - and the remedy that fixes one does nothing for t…

• Events per Variable and the Minimum Sample Size for Regression

  The EPV 10 rule for regression sample size, where it came from, and the Riley et al. (2019) performance-based criteria that replaced it for prediction…

• Exploratory Factor Analysis: Extraction, Rotation, and How Many Factors to Retain

  Exploratory factor analysis turns on three decisions usually made by accepting a default: extraction method, number of factors retained, and rotation.…

• Exponential Smoothing Formula: Worked Example and How to Choose Alpha

  The exponential smoothing formula with every symbol, both index conventions, a 12-point worked example and what error says about choosing alpha.…

• Factorial Designs: Testing Multiple Factors and Their Interactions at Once

  A full factorial design tests every combination of two or more factors at once instead of one factor at a time -- more efficient, and the only way to…

• Futility Analysis and Conditional Power: When to Stop a Trial for Futility

  How conditional power is calculated at an interim look, why the assumed treatment effect changes the answer, and why non-binding futility rules cannot…

• Fuzzy Regression Discontinuity: The Instrumental-Variables Interpretation

  Fuzzy RDD explained: why imperfect compliance at the cutoff turns regression discontinuity into an instrumental-variables problem, what LATE it identi…

• Generalized Pareto Distribution: Formula, Worked Example and Code

  Generalized Pareto distribution formula, tail types and a worked exceedance and return-level calculation, with SciPy and MATLAB fitting code.…

• Granger Causality: What It Does and Does Not Show

  Granger causality tests whether one time series improves prediction of another — a narrower claim than true causation. This guide covers the two main…

• How to Choose a Statistical Test: A Decision Tree

  A navigable decision tree for picking the right statistical test from four questions -- outcome type, group count, paired or independent, and whether…

• How to Interpret a Hazard Ratio (and Why It Is Not a Risk Ratio)

  A hazard ratio from a Cox proportional-hazards model is an instantaneous rate ratio, not a risk ratio. This guide walks through a worked example, a pl…

• How to Read a Kaplan-Meier Curve: Censoring Marks, Median Survival, and Risk Tables

  How to correctly read a Kaplan-Meier survival curve: what the censoring tick marks mean, why the line is a step function, how to read off median survi…

• Instrumental Variables: The Three Assumptions and the Weak-Instrument F-Test

  The three IV assumptions (relevance, exclusion restriction, exogeneity), what violates each, the first-stage F-statistic and the F>10 weak-instrument…

• Inter-Rater Reliability: Choosing the Right Coefficient

  A selection table from data type and rater design to the right agreement statistic - Cohen's and weighted kappa, Fleiss' kappa, ICC, Krippendorff's al…

• Interim Analyses in Clinical Trials: Why Pre-Specification Comes First

  What counts as an interim analysis, why an unplanned look inflates Type I error even without intent to influence the trial, and what has to be pre-spe…

• Interpreting Bayes Factors: Evidence Scales and What They Mean

  The Jeffreys (1961) and Kass-Raftery (1995) scales for reading Bayes factor magnitude, a reproducible worked example, and why a Bayes factor -- unlike…

• Interrupted Time Series Analysis: Segmented Regression, Autocorrelation, and Lag

  Interrupted time series analysis uses segmented regression to separate an immediate level change from a slope change at the intervention point. This g…

• Inverse Probability Weighting: When It Beats Propensity Score Matching

  IPW reweights the full sample instead of discarding unmatched units the way propensity score matching does — a real advantage that comes with its own…

• Latin Square Designs: Controlling Two Blocking Factors at Once

  A Latin square design blocks on two nuisance variables at once (rows and columns) instead of one, using an n×n grid where every treatment appears exac…

• Likelihood Ratio Test for Nested Models

  A complete guide to the likelihood ratio test for comparing nested maximum-likelihood models: why the nesting requirement invalidates non-nested compa…

• Likert Scale Survey Design & Statistical Analysis Guide

  How to design and analyse Likert data: the difference between a Likert item and a Likert scale, how 5-point, 7-point and forced-choice formats behave,…

• Markov Chain Monte Carlo (MCMC): What It Is and How to Read the Diagnostics

  What MCMC does, how Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo/NUTS work, and the diagnostics (R-hat, ESS, trace plots, divergen…

• Mathematics vs Statistics: What Is the Difference?Comparison

  Mathematics proves results by deduction from assumptions; statistics draws conclusions from data under uncertainty. Side-by-side comparison of methods…

• Mediator vs. Moderator: Which One Your Hypothesis Actually NeedsComparison

  Would changing X change the variable? If yes it is a mediator and you bootstrap the indirect effect ab. If not it is a moderator and you test the prod…

• Net Reclassification Improvement (NRI): The Calculation, and the Case Against It

  The NRI compares two risk prediction models by counting who moved in the right direction. This guide gives the arithmetic for both the category-based…

• No Correlation Examples: What Zero Correlation Looks Like (and When It Misleads)

  Worked no-correlation examples: unrelated data, a perfect parabola with r = 0, and a flat line where r is undefined, with APA reporting and code.…

• Non-Response Bias: How to Measure It and What to Report

  Response rate is a weak proxy for non-response bias. Three measurement procedures — wave analysis, a non-respondent follow-up sub-sample, and benchmar…

• Normality of Distribution: How to Check the Normal Distribution Assumption in Research Data

  What the normal distribution is, why the Central Limit Theorem makes most explanations of it misleading, and how to actually assess normality using Q-…

• P-Value vs Confidence Interval: What Each One Actually Tells YouComparison

  A p-value tells you how surprising your data would be if there were truly no effect. A confidence interval tells you the range of effect sizes your da…

• Permutation Tests: When They Beat Parametric Tests

  Permutation tests build their own null distribution by reshuffling group labels instead of assuming a normal population — a worked example shows when…

• Poisson Distribution: Formula, Assumptions, and When to Use It for Count Data

  How to recognize count data that fits a Poisson distribution, the formula and its single parameter (lambda), the four assumptions that must hold, work…

• Power Analysis and Sample Size Calculation: A Complete Guide

  How to run a power analysis to calculate sample size: the four interlocking quantities (effect size, alpha, power, N), a priori vs. post-hoc power, ch…

• Prior Sensitivity Analysis

  A prior sensitivity analysis re-runs a Bayesian model under a small set of alternative, defensible priors to check whether the conclusion that matters…

• Propensity Score Matching: How It Works and What It Cannot Fix

  Propensity score matching balances measured covariates between treated and untreated groups — it cannot address confounding from variables that were n…

• Q-Values and the Positive False Discovery Rate: The FDR Analog of a P-Value

  The q-value is the FDR analog of a p-value — the minimum false discovery rate at which a test is called significant. Defines it against the Benjamini-…

• Quantitative vs Categorical Variables: Differences and ExamplesComparison

  A categorical variable sorts items into groups; a quantitative variable records a count or measurement. See the test, the numeric-code trap and worked…

• Questionnaire Design: Writing Survey Questions That Work

  A construction guide to the questionnaire-design decisions that determine data quality: question wording, response-option and Likert scale design, que…

• Ratio Scale Examples: What It Is, Formula and Worked Example

  Ratio scale examples, the a/b formula and a worked calculation, with a true-zero test and Excel, R and Python code.…

• Regression Analysis: Assumptions, Interpretation, and How to Report It

  A practical guide to choosing between linear, logistic, and other regression models, checking each model's assumptions, interpreting coefficients and…

• Regression Discontinuity Design: Bandwidth, Running Variable, and the McCrary Test

  A practical guide to regression discontinuity design: sharp vs. fuzzy RD, what makes a running variable valid, the bias-variance trade-off in bandwidt…

• Restricted Mean Survival Time (RMST): The Hazard-Ratio Alternative When Proportional Hazards Fails

  Restricted mean survival time (RMST) is the area under the Kaplan-Meier curve to a fixed time point -- a model-free alternative to the hazard ratio wh…

• Sample Size for Survival Analysis: Why Events, Not People, Drive Power

  Survival-analysis power depends on the number of events, not total enrollment. The Schoenfeld formula, converting events to an enrollment target via a…

• Sample Size Justification for a Grant Application or IRB Protocol

  How to justify a sample size to a grant reviewer or IRB, not just calculate one: the NIH Approach criterion, 45 CFR 46 risk/benefit review, the pilot-…

• Sampling Bias: Types, Causes, and How to Detect It

  Sampling bias occurs when a sample systematically differs from the population it should represent, distorting results in a direction that more data ca…

• Sampling Distribution: Definition, Standard Error & CLT

  What a sampling distribution is, how it differs from a population or sample distribution, and why standard error and the Central Limit Theorem make st…

• Sensitivity Power Analysis: Finding the Minimum Detectable Effect Size

  How to compute the minimum detectable effect size (MDES) for a fixed, already-collected sample size, using G*Power's sensitivity mode or R's pwr packa…

• Shapiro-Wilk Test in R: shapiro.test(), Its Sample-Size Limits, and When Not to Use It

  How to run shapiro.test() in R, why it stops at n = 5000, why W and n must be reported together, and why using the p-value to pick between a t-test an…

• Skip Logic and Branching in Questionnaire Design

  Designing and documenting survey skip logic and branch conditions: avoiding orphaned items, routing on stable field IDs rather than question position,…

• Statistical Power Analysis & G*Power Sample Size Guide

  How to size a study properly: the interlocking relationship between alpha, power, effect size and sample size, the difference between a priori, sensit…

• Statistical Significance: What the Verdict Means and Doesn't Mean

  A decision-framework guide to statistical significance: what the verdict asserts, how the alpha threshold is chosen, why sample size distorts it, the…

• Structural Equation Modeling (SEM): Fit Indices, Cut-Offs and Model Evaluation

  A judgment-layer guide to evaluating structural equation models: which fit indices to report, why the widely cited Hu and Bentler cut-offs differ from…

• Summarizing a Posterior Distribution: Point Estimates, Credible Intervals, and HDI vs. Equal-Tailed Intervals

  A guide to summarizing a Bayesian posterior distribution: point estimates (mean, median, mode), credible intervals, and why HDI vs. equal-tailed inter…

• Survey Mode Effects: Selection vs. Measurement Effects in Mixed-Mode Design

  Mixed-mode surveys combine mode selection effects (who chooses which mode) with mode measurement effects (how the mode itself changes answers). This g…

• Survey Weighting: Design Weights, Post-Stratification, and Raking

  Design weights correct for unequal selection probability, post-stratification corrects for a known population-margin mismatch, and raking (iterative p…

• Survival Analysis: Choosing Between Kaplan-Meier, Cox, and Parametric Models

  A decision framework for choosing Kaplan-Meier, Cox regression, or a parametric survival model based on the research question you are actually asking,…

• Synthetic Control Method: Constructing a Weighted Counterfactual for a Single Treated Unit

  A synthetic control builds a weighted composite of untreated donor units to serve as a counterfactual for a single treated unit. This guide covers don…

• Test-Retest Reliability: Choosing the Retest Interval, and Checking for Drift

  The retest interval is the design decision a test-retest coefficient cannot recover from. What the interval trades off, what COSMIN actually asks abou…

• The Central Limit Theorem: Why Sample Means Go Normal

  The Central Limit Theorem explains why the distribution of sample means approaches normal as sample size increases, regardless of the population’s sha…

• The Kolmogorov-Smirnov Test: One-Sample vs Two-Sample, and the Estimated-Parameter Trap

  The one-sample Kolmogorov-Smirnov test and the two-sample test answer different questions and have different validity conditions. The one-sample versi…

• The Wilcoxon Signed-Rank Test: Assumptions, Exact vs. Normal Approximation, and How to Report It

  A decision guide to the Wilcoxon signed-rank test: what its null actually asserts (symmetry of the differences, not equal medians), why it is the pair…

• Trimmed Mean Formula: How to Calculate It, With Examples

  The trimmed mean formula explained: sort, cut k values from each end, average the rest. Worked example plus Excel, R and Python differences.…

• What Does Alpha Mean in Statistics? Significance Level Explained

  Alpha (α) is the significance level: the Type I error rate you accept. See how it sets the p-value cutoff, with a worked example and code.…

• What Is a P Value? Definition, Misinterpretations, and How to Read One

  What a p value actually measures, the four most common misinterpretations (it is not the probability the null is true, not the probability of chance,…

• What Is Data Science? Research Areas, Funding, and Career Paths

  What data science studies, its major subfields, who funds the research (NSF, NIH ODSS), core research methods and tools, and typical career and PhD pa…

• What Is Health Informatics? Fields, Methods & Careers

  Health informatics is the field that uses data, information, and technology to improve health and care delivery. This guide separates it from biomedic…

• Z-Test vs T-Test: Why the t-Test Wins at Every Sample Size, and Where a Z-Test Genuinely BelongsComparison

  The z-test needs a known population standard deviation, which real research never has. Why the t-test is correct at every n, how fast the two converge…

Research Tools & Software

• 3D Plots in R: scatterplot3d, rgl and persp

  How to make 3D plots in R with scatterplot3d, rgl and persp: which to choose, CRAN install, working code, fitted planes and how to read the angles.…

• Creating Dummy Variables in Stata: i. Factor Notation vs. tabulate, generate()

  Two ways to create dummy/indicator variables in Stata — the i. factor-variable operator vs. tabulate, generate() — and when each one is the right tool…

• Data Analysis Tool in Python: pandas and SciPy Guide

  Install pandas and SciPy from official sources, run load, describe, groupby and a t-test, and learn how to read each output, including equal_var.…

• Eurostat: Using the EU Statistical Office's Data Browser and API for Research

  How researchers use Eurostat, the EU statistical office: Data Browser, Statistics API URL pattern, reading JSON-stat id and size, reproducible pulls.…

• For Loops in R: Syntax, break/next, and When to Use lapply

  How for loops work in R, per the R reference: syntax, seq_along, break and next, why results must be stored, and when lapply, sapply or vapply fits be…

• ggplot Line Types: linetype Values, Hex Patterns and Scales

  ggplot line types explained: the seven linetype values, custom hex dash patterns, linetype scales, reference lines and fixes for common errors.…

• How Do You Make a Scatter Plot in Excel? Steps and Trendline

  Step-by-step guide to making a scatter plot (scatter graph or scattergram) in Excel, with axis titles, a trendline and fixes for common mistakes.…

• How to Calculate SEM in Excel: Formula, Steps and Error Bars

  How to calculate SEM in Excel with STDEV.S, COUNT and SQRT, a checkable worked example, and the menu path for custom mean ± SEM error bars.…

• import scipy as sp: What the Alias Does and How to Import Submodules

  What import scipy as sp does, what the SciPy docs recommend instead, and how to fix the missing-submodule AttributeError.…

• Jupyter Notebook LaTeX: Math Syntax and Export

  A guide to writing LaTeX math in Jupyter Notebook markdown cells, displaying dynamic equations with IPython, and exporting notebooks to LaTeX via nbco…

• Logistic Regression in R: glm(), Odds Ratios, and Diagnostics

  A runnable R workflow for logistic regression: glm(family = binomial), exponentiating coefficients to odds ratios, why confint() profile intervals dif…

• MatchIt in R: Install, matchit() Options, Balance Output

  Install MatchIt from CRAN, run a worked matchit() example, map options like replace, mahvars and caliper to questions, and read balance output.…

• NumPy full: Create an Array Filled With a Value

  How np.full builds an array of one value: the signature, dtype inference, NaN preallocation, list fill values and the full_like truncation trap.…

• numpy.linalg.norm: Syntax, ord Values and Axis Examples

  How numpy.linalg.norm works: the full ord table for vectors and matrices, axis and keepdims, and the 2-D trap, plus vector_norm, lstsq and inv.…

• Pipe in R: |> vs %>% With Examples

  Pipe in R explained: native |> (R 4.1.0+) vs magrittr %>%, the _ placeholder, and fixes for common pipe errors.…

• Poisson and Negative Binomial Regression in Stata

  Fitting poisson and nbreg for count outcomes in Stata: using exposure() to model rates, testing for overdispersion, and reporting results as incidence…

• Propensity Score Matching in Stata: psmatch2 vs. teffects psmatch

  Why psmatch2 (user-written) and teffects psmatch (official) are not interchangeable in Stata: syntax differences, the standard-error gap Abadie and Im…

• SciPy Rotate: ndimage.rotate Image Rotation, reshape and order

  SciPy rotate explained: ndimage.rotate for arrays and images, what reshape, order and mode do, and when to use spatial.transform.Rotation for 3D.…

• Seaborn Boxplot: sns.boxplot Syntax, Parameters and How to Read It

  How to draw a seaborn boxplot with sns.boxplot: syntax, v0.13 parameters, what the whiskers mean, and which options pass through to matplotlib.…

• Seaborn Pairplot: sns.pairplot Syntax, Parameters and How to Read It

  How to use seaborn pairplot: syntax, parameters and defaults in 0.13.2, a runnable penguins example, corner plots, and when to switch to PairGrid.…

• SPSS Syntax: Writing Reproducible Analysis Files

  How to use the Paste button to build a runnable .sps syntax file, comment it well, and run it from a clean, unmodified dataset so an SPSS analysis is…

• Stata Do-Files: Structure for a Reproducible Workflow

  The do-file header, comment styles, and file-path conventions that make a Stata analysis reproducible: version, clear all, set more off, log using, an…

• Weibull Analysis: Research Software Guide

  Learn Weibull analysis in research using Python reliability: fit shape and scale parameters, analyze censored data, and interpret B10 component life c…

• What Is a Vector in R? Creating, Indexing and Recycling

  A vector in R is an ordered collection of values. Learn to create, index and combine vectors, and how recycling works, with examples from the R manual…

• What Is Overfitting? How to Detect It with Validation and Learning Curves

  Overfitting means a model fits its training data but fails on new data. Learn the scikit-learn rule and how to read validation and learning curves.…

• What Is PyCharm? A Python IDE for Research Code

  PyCharm is JetBrains’ Python IDE. See the current unified edition, system requirements, how to run a script and a Jupyter notebook, and free alternati…

• What Is Sentiment Analysis? Methods, Python and R Steps, and Limits

  Sentiment analysis classifies text as positive, negative or neutral. Learn the methods, Python and R steps from official docs, and the limits to repor…

Clinical Research Administration

• Adaptive Design in Clinical Trials: Types, FDA Guidance & Key Considerations

  A guide to adaptive clinical trial design: what qualifies as an adaptive design, common adaptation types (sample size re-estimation, dropping arms, re…

• Designing a Clinical Trial: Endpoints, Sample Size, Randomization, SAP

  A practical guide to the core mechanics of clinical trial design: choosing primary and secondary endpoints, calculating sample size and power, selecti…

• Frequentist vs. Bayesian Statistics in Clinical TrialsComparison

  Frequentist trial design fixes a sample size and tests against a null hypothesis via p-values. Bayesian design uses prior distributions updated with t…

• ICH E17: General Principles for Planning and Design of Multi-Regional Clinical Trials

  ICH E17 governs how sponsors plan and design a single clinical trial protocol usable for regulatory submission across multiple regions at once. Adopti…

• ICH E20: The Harmonized Guideline on Adaptive Designs for Clinical Trials

  ICH E20 is the draft ICH-harmonized guideline on adaptive designs for confirmatory clinical trials -- currently at Step 2b. This guide covers its scop…

• SPIRIT 2025: Writing a Clinical Trial Protocol (The Full Checklist)

  SPIRIT is the reporting checklist for clinical trial protocols -- the CONSORT counterpart for the protocol stage. This guide covers the SPIRIT 2025 st…

• Statistical Analysis Plan Template for Clinical Trials

  A standard, section-by-section statistical analysis plan (SAP) template for clinical trials, showing exactly where ICH E9(R1) estimand attributes, ana…

• Statistical Significance vs. Clinical SignificanceComparison

  Statistical significance (a p-value below a chosen threshold) tells you an effect is unlikely to be chance. Clinical significance (effect size measure…

• Surrogate Endpoint Validation in Clinical Trials: The Regulatory Framework

  A research-administrator's guide to surrogate endpoint validation: biological plausibility, the Prentice criteria, meta-analytic validation, and how F…

• Target Trial Emulation: Applying RCT Design Principles to Real-World Data

  What target trial emulation is, the seven-component Hernán & Robins protocol framework, the biases it addresses (immortal time bias, confounding by in…

Research Data Management

• Copernicus Climate Data Store (C3S): Accessing Europe's Climate Reanalysis and Projection Data

  How to access the Copernicus Climate Data Store (CDS): what the Copernicus Climate Change Service (C3S), operated by ECMWF, provides — ERA5 reanalysis…

• Design of Experiments (DOE): A Guide for Researchers

  Design of Experiments (DOE) is the statistical methodology for planning controlled tests so factor effects can be estimated efficiently. This guide co…

• Differential Privacy for Research Data Release

  What differential privacy means, a worked numeric example, how to choose an epsilon privacy budget, and when it should replace suppression for releasi…

• Environmental Data Initiative (EDI): The LTER Network’s Data Repository

  EDI is the repository that publishes LTER Network and broader environmental/ecological data as EML-described data packages — distinct from the EML sta…

• k-Anonymity and Disclosure Risk in Shared Datasets

  How k-anonymity, l-diversity and t-closeness quantify re-identification risk in a dataset before it is shared, with a worked example and how they rela…

• NEON (National Ecological Observatory Network): Continental-Scale Ecological Data for Researchers

  NEON is an NSF-funded, Battelle-operated observatory producing standardized, long-term ecological and environmental sensor data across 81 U.S. field s…

• NOAA NCEI: Accessing the National Centers for Environmental Information's Climate and Ocean Data

  How to access NOAA's National Centers for Environmental Information (NCEI): its role as NOAA's climate, weather, and ocean data archive, its predecess…

• Stratified Sampling: Definition, Method, and When to Use It

  How stratified sampling divides a population into subgroups (strata) for independent random sampling, the difference between proportional and dispropo…

• Trusted Research Environments and the Five Safes Framework

  A practical guide to trusted research environments (TREs): what the Five Safes framework assesses, how the researcher accreditation and project-approv…

• What Is SPSS? IBM SPSS Statistics Explained for Researchers

  SPSS (IBM SPSS Statistics) is the statistics package built for social-science and health research. This guide explains what it does, the Statistics/Mo…

Scholarly Publishing

• American Statistical Association (ASA): Research and Practice Guide

  Explore the American Statistical Association (ASA): journals, PStat accreditation, ethical guidelines, JSM conferences, and member sections in statist…

• ASA Ethical Guidelines for Statistical Practice: Guide

  An overview of the ASA Ethical Guidelines for Statistical Practice, covering data integrity, algorithmic transparency, and ethical statistical researc…

• Bayesian vs. Frequentist Network Meta-AnalysisComparison

  Bayesian and frequentist network meta-analysis fit the same underlying model differently: priors and MCMC convergence checks vs no priors and p-values…

• International Statistical Institute (ISI): Research Guide

  Explore the International Statistical Institute: associations, journals, World Statistics Congress, membership tiers, and research-practice standards.…

• Joint Statistical Meetings (JSM): Structure, Editions, and Submission Guide

  Complete guide to the Joint Statistical Meetings (JSM): partner societies, attendance scope, JSM 2026 in Boston, JSM 2027 in Chicago, and submission r…

• P-Curve Analysis: Testing for Evidential Value vs. P-Hacking

  P-curve analysis examines the distribution of significant p-values in a literature to test whether findings reflect genuine effects or selective repor…

• Royal Statistical Society: Structure, Journals, and Accreditations

  An overview of the Royal Statistical Society (RSS), its peer-reviewed journals (JRSS Series A, B, C), professional CStat accreditation, and resources.…

• Statistics Without Borders: Pro Bono Data Guide

  Statistics Without Borders connects volunteer statisticians with non-profits and researchers for pro bono analytics, survey design, and evaluation.…

• Trial Sequential Analysis: Monitoring Boundaries for Cumulative Meta-Analysis

  How Trial Sequential Analysis adapts group sequential monitoring boundaries and required information size to cumulative meta-analysis, and what the Co…

Scholarly Writing

• APA Statistics Reporting: Formats and Rules for APA 7th Edition

  Complete guide to reporting statistics in APA 7th Edition style, covering t-tests, ANOVA, regression, p-values, effect sizes, and bracketed confidence…

• How to Report P Values: APA Formatting Rules and Current Best Practice

  How to report p values in APA style: exact-value vs. threshold reporting, why p = .000 is always wrong (use p < .001), and why current standards ex…

• Justifying Sample Size in a Manuscript: How to Write the Methods-Section Case

  How to write the sample-size justification a journal's Methods section expects: summarizing a power calculation in prose, stating effect-size assumpti…

Patient Safety & Infection Prevention

• Choosing the Right Control Chart in Healthcare: A Decision Guide by Data Type

  A decision guide to picking the right SPC control chart for a hospital quality measure: p-chart for a compliance rate, u-chart for an infection rate p…

• Run Charts for Quality Improvement: The Four Rules for Real Signal

  The four probability-based run-chart rules — shift, trend, too many/too few runs, and the astronomical point — with exact counting criteria and a work…

Grants Management & Research Funding

• COPSS Award: Eligibility & Nomination Guide

  Understand the COPSS Award criteria, nomination dossier requirements, and key distinctions from the International Prize in Statistics for researchers.…

Laboratory Operations & Safety

• Incoming Inspection Sampling Plan: How to Set One Up (ANSI/ASQ Z1.4)

  How to build a risk-based incoming inspection sampling plan for supplier lots using ANSI/ASQ Z1.4, ISO 2859-1, and zero acceptance number (c=0) approa…

Research Information & Identifiers

• OpenAlex: What It Is and How It Works

  A practical guide to OpenAlex: the free, CC0 scholarly metadata graph from OurResearch that succeeded Microsoft Academic Graph — its core entities, AP…

Research Integrity & Compliance

• The Resource Equation Method for Animal Sample Size

  How the resource equation method (E = total animals - total groups, targeted at 10-20) sizes small animal experiments, when it is appropriate versus w…

Key terms

• Blinding and Masking in Clinical Trials
• Five Safes framework
• Harvard Data Science Review (HDSR) Metrics
• ICH E9 (Statistical Principles for Clinical Trials)
• P-Value
• Randomization Methods in Clinical Trials
• Secure Data Enclave
• Simulation of Clinical Trials
• Statistical Analysis Plan (SAP)
• Statistical Test
• The American Statistician
• Trusted research environment