- "Guide to Advanced Empirical Software Engineering" by Forrest Shull, Janice Singer, and Dag I. K. Sjøberg.
- "How To Write A Better Thesis" by David Evans, Justin Zobel, and Paul Gruba.
- "Writing for Computer Science" by Justin Zobel.
- "Contemporary Empirical Methods in Software Engineering" by Michael Felderer and Guilherme Horta Travassos.
- "PhD Completion Milestones" by Laurie Williams.
- "How to write a great research paper" by Simon Peyton Jones.
- "How to do good research" by Frédo Durand. Part of Resources for Students & Scholars.
- "Writing Research Papers" by Aaron Hertzmann. Look at "Advice for Graduate Students" and "Graduate Skills Seminars, 2010-2011" as well.
- "How to do research" by William T. Freeman.
- Researcher guidelines for Linux
- "Writing a scientific article: A step-by-step guide for beginners"
- I ****ing hate Science
- "Writing Well" by Julian Shapiro.
- "Writing a book: is it worth it?" by Martin Kleppmann.
- "How to write a scientific abstract in six easy steps".
- "Three Sins of Authors in Computer Science and Math" by Jonathan Shewchuk.
- APA Style Blog.
- "Writing Tips for IEEE Software" by IEEE.
- "Mathematical Writing" by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts. The PDF version is here. All the lectures are also on YouTube.
- "How (and How Not) to Write a Good Systems Paper" by Roy Levin and David D. Redell. A second copy is here.
- "How to Get a Paper Accepted at OOPSLA" by Kent Beck.
- "The Science of Scientific Writing" by George D. Gopen and Judith A. Swan, In American Scientist, Vol. 78, No. 6 (Nov–Dec, 1990), pp. 550–558.
- "On writing" by Terence Tao.
- The Grammar of Mathematics: Writing About Variables
- How to write the introduction
- "The Elements of Style" by William Strunk Jr. and E. B. White.
- Either "The Dictionary of Concise Writing: 10,000 Alternatives to Wordy Phrases" or "To the Point: A Dictionary of Concise Writing" by Robert Hartwell Fiske.
- "Bugs in Writing: A Guide to Debugging Your Prose" by Lyn Dupre.
- "Advice for writing LaTeX documents".
- "style-check for LaTex. Good references for more concise writing.
- Common errors in bibliographies
- Keeping tables/figures close to where they are mentioned
- Basic diagram examples
- LaTeX/Presentations
- LaTeX Books and Articles
- How to Write a Minimalistic CV in LaTeX
- Self-publishing your (LaTeX) thesis
- LaTeX book with examples, open-source eBook
- "Hints for Reviewing Empirical Work in Software Engineering" by Walter F. Tichy. Direct PDF.
- "Reviewing as an Essential Component of a Researcher’s Life" by Massimiliano Di Penta.
- "Have You Seen This Review?" by Dirk Riehle.
- "Writing an effective response to a manuscript review".
- It's just going through the motions ... "The Toxic Culture of Rejection in Computer Science" by Edward Lee.
- IEEE Post-Publication Policies
- IEEE: Can I reuse my published article in my thesis?
- ACM Author Rights
- Can I include Figures and Texts from IEEE/ACM publications in my PhD thesis?
- NIST/SEMATECH e-Handbook of Statistical Methods
- "Handbook of Biological Statistics" by John H. McDonald. https://www.youtube.com/@datatab/videos
- "DATAtab YouTube channel"
- "StatQuest with Josh Starmer YouTube channel"
- "Biostatistics YouTube channel"
- "Biostatistics Book Series" by Frans Rodenburg.
- Cochran–Armitage test for trend
- A Look at Precision, Recall, and F1-Score by Teemu Kanstrén.
- How should one interpret the comparison of means from different sample sizes?
- 30 Samples. Standard, Suggestion, or Superstition?
- Science and Cookies
- Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
- Correlation
- Correlation by Pavan Akula.
- User's guide to correlation coefficients by Haldun Akoglu.
- Correlation Coefficients
- Pearson Product-Moment Correlation
- Types of Variables, Descriptive Statistics, and Sample Size
- Everything You Need To Know About Correlation
- What does recall mean in Machine Learning?
- Power Analysis
- Power Analysis in Experimental Design
- Simulation for Power Analysis
- Power to the People: Power, Negative Results and Sample Size by Brianna N Gaskill and Joseph P Garner.
- Kruskal–Wallis Test
- Rigorous definition of an outlier?
- Dunn's test
- Descriptive Statistics and Normality Tests for Statistical Data
- Multiple comparisons with many groups
- Review of Taleb, Nassim Nicholas. The Black Swan: The impact of the highly improbable.
- What references should be cited to support using 30 as a large enough sample size?
- Precision or Recall: Which Should You Use?
- The Chi-square test of independence by Mary L. McHugh.
- "The ASA Statement on p-Values: Context, Process, and Purpose" by Ronald L. Wasserstein and Nicole A. Lazar.
- Bad statistical practice in pharmacology (and other basic biomedical disciplines): you probably don't know P by Michael J. Lew.
- Statistical Inference in the 21st Century: A World Beyond p < 0.05. The American Statistician, Volume 73, Issue sup1 (2019).
- "Moving to a World Beyond "p < 0.05" by Ronald L. Wasserstein, Allen L. Schirm, and Nicole A. Lazar.
- "An investigation of the false discovery rate and the misinterpretation of p-values" by David Colquhoun.
- "Do multiple outcome measures require p-value adjustment?" by Ronald J. Feise.
- "Understanding p-values and the Controversy Surrounding Them" by Jessica Utts.
- "P values and statistical practice" by Andrew Gelman.
- "Odds Are, It’s Wrong" by Tom Siegfried.
- "Scientists rise up against statistical significance" by Valentin Amrhein, Sander Greenland, and Blake McShane.
- "Things I Have Learned (So Far)" by Jacob Cohen. Direct PDF.
- "Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach" by Giovanni Di Leo and Francesco Sardanelli.
- "Statistics Done Wrong. The woefully complete guide" by Alex Reinhart.
- "P – Value, a True Test of Statistical Significance? A Cautionary Note" by Tukur Dahiru.
- "On reporting and interpreting statistical significance and p values in medical research" by Herman Aguinis, Matt Vassar, and Cole Wayant.
- "p < 0.05, < 0.01, < 0.001, < 0.0001, < 0.00001, < 0.000001, or < 0.0000001 …" by Weimo Zhu.
- "Understanding results: P-values, confidence intervals, and number need to treat" by Lawrence Flechner and Timothy Y. Tseng.
- "How to control confounding effects by statistical analysis" by Mohamad Amin Pourhoseingholi, Ahmad Reza Baghestani, and Mohsen Vahedi.
- "A practical guide to methods controlling false discoveries in computational biology" by Keegan Korthauer et al.
- "Using false discovery rates for multiple comparisons in ecology and evolution" by Nathan Pike.
- Mann-Whitney U-Test
- "Good explanation about Mann-Whitney U Test."
- "Two-sample Mann–Whitney U Test" by Salvatore S. Mangiafico.
- "Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 1: general issues and tail-area-based methods" by Robert G. Newcombe.
- "Confidence Intervals of the Mann-Whitney Parameter that are Compatible with the Wilcoxon-Mann-Whitney Test" by Michael P. Fay1 and Yaakov Malinovsky.
- Tests for Ordinal Categorical Data
- Mann Whitney U Test (Wilcoxon Rank Sum Test)
- Andreas Zeller
- Arie van Deursen
- Austin Z. Henley
- Ayushi Rastogi
- Christian Bird
- Christopher J Parnin
- Dan Luu
- Diomidis Spinellis
- Eric Lippert
- Gerard J. Holzmann. One of the few researchers whose papers indicate that he has actually worked on real-world software. Just read everything he has published.
- Jaideep Ganguly
- Lorin Hochstein
- Massimiliano Di Penta
- Nachiappan (Nachi) Nagappan
- Ted Unangst
- Terence Tao
- Thomas Zimmermann
- Empirical Software Engineering. Look at Publish Open Access in EMSE
- IEEE Transactions on Software Engineering
- Journal of Systems and Software
- The long list of SE conferences and journals. Direct link to Google Sheets file.
- Springer Nature and their OA agreement for the Netherlands. And more details.
- Latest Software Engineering papers from arXiv.
- "Getting a Computer Science PhD in the USA".
- "PhD Completion Milestones".
- "The illustrated guide to a Ph.D.". It's worth reading all the other content from Matt Might as well.
- "A Reading List for PhD Students (and their Supervisors)"
- "Open Science Policies" by Daniel Graziotin.
- "The Ph.D. Grind. A Ph.D. Student Memoir" by Philip J. Guo. See if you can find a PDF copy somewhere. All the existing links are dead.
- Where to publish?
- Mars Code: building robust software
- https://titlecaseconverter.com/
- https://capitalizemytitle.com/
- https://zbib.org/
- https://www.doi2bib.org/
- arxiv-latex-cleaner
- https://www.scopus.com/
- ISBN to BibTeX converter
- Free images: https://unsplash.com/
- Free images: https://pixabay.com/
- Presentation templates: https://www.canva.com/
- "Design 101 lecture" by Michele Lanza.
- "How to Get Your Research Adopted" by Emery D. Berger.
- "Salary Negotiation: Make More Money, Be More Valued" by Patrick McKenzie .
- "Ten Rules for Negotiating a Job Offer" by Haseeb Qureshi.
- "Questionnaires and Surveys: Analyses with R" by Martin Schweinberger.
- "Survey Data Analysis with R"
- "Using R to Analyze & Evaluate Survey Data" by Demetrius K. Green.
- "Survey analysis" from The Epidemiologist R Handbook.
- "On Likert Scales In R" by Jake Chanenson.
- "Do not use averages with Likert scale data" by Dwight Barry.
- "Data Vis for Likert Questions" by Laura Mudge.
- "Using likert on summary results" by M. Devlin.
- "Qualitative Analysis".
- "Worse Than Spam: Issues In Sampling Software Developers" by Sebastian Baltes and Stephan Diehl.
- Is a Likert-type scale ordinal or interval data?
- can likert scale data ever be continuous? by Karen Grace-Martin.
- Analyzing ordinal data with metric models: What could possibly go wrong? by Torrin M. Liddell, John K. Kruschke.
- Introduction to Likert Data
- JABSTB: Statistical Design and Analysis of Experiments with R
- "Summary and Analysis of Extension Program Evaluation in R" by Salvatore S. Mangiafico.
- "R for Data Science" by Hadley Wickham and Garrett Grolemund.
- "Wilcoxon test in R" by Antoine Soetewey.
- "Chi-square test of independence in R" by Antoine Soetewey.
- Chi-squared Test of Independence
- Multiple linear regression made simple by Antoine Soetewey.
- "Data Exploration: The Titanic"
- "How to Perform Correlation Analysis in Time Series data using R?" by Luciano Oliveira Batista.
- "Testing for time trend" by Ken.
- "R Cookbook, 2nd Edition" by James (JD) Long and Paul Teetor.
- Contingency Tables
- R - QQPlot: how to see whether data are normally distributed
- Why is message() a better choice than print() in R for writing a package?
- Aggregating and analyzing data with dplyr
- Scatterplots
- Bar Plots
- ggplot2 axis ticks : A guide to customize tick marks and labels
- GGPlot Axis Limits and Scales
- ggsave : Save a ggplot - R software and data visualization
- Normality Test in R
- Outliers detection in R
- Compute Summary Statistics in R
- Group by one or more variables
- R - Scatterplots
- How to Detect,Impute or Remove Outliers from a Dataset using Percentile Capping Method in R
- Descending order
- Logarithmic scale in R with ggplot2
- Basic Plotting With ggplot2
- ANOVA in R
- How to Filter in R: A Detailed Introduction to the dplyr Filter Function
- Data visualization with ggplot2
- GGPlot Log Scale Transformation
- Annotation: log tick marks
- How to Remove Outliers in R
- Correlation Test Between Two Variables in R
- Using Dates and Times in R
- ggplot2 scatter plots : Quick start guide - R software and data visualization
- Machine Learning with R: A Complete Guide to Decision Trees
- Chi-Square Test of Independence in R
- Decision Tree in R: Classification Tree with Example
- Spearman Rank Correlation
- Correlation tests Using R
- Statistical Analysis
- Visualizing data with ggplot2