Expert guides and insights for your PG journey
Learn from the experiences of successful doctors. Get practical tips for training, exam preparation, thesis writing, and career planning.
Stateazy — Biostatistics Made EasyComplete analysis of challenges faced by PG+SS trainees during training, exams, and post-PG career. Learn from others' experiences to prepare better.
How anger towards juniors, seniors, and faculty drains your mental energy and impacts exam success. Learn the forgiveness framework for PG+SS trainees.
Everything you need to know for exam day - time management (16 min per question), food, sleep, reaching center early, and writing answers that examiners love.
Official NBEMS guidelines for thesis protocol, writing, submission, and modifications. Word limits, IEC/SRC composition, fees, deadlines, and step-by-step instructions.
Real-world problems and solutions for your thesis journey. Guide selection, sample size calculation, IEC approval, payment issues, modified thesis, plagiarism, and emergency contacts.
The uncomfortable truths about career planning, city-wise salary expectations, AYUSH/quack competition, cut practice, building referrals, and why your NEET rank doesn't matter anymore.
Why 75% of doctors think they communicate well but only 21% of patients agree. Breaking the cycle of yelling, handling WhatsApp misinformation, avoiding medical gaslighting, and rebuilding empathy.
AI can diagnose better than you. CAs earn as much as you. The only thing that makes you irreplaceable is human connection. Breaking the cycle of abuse, ego, and toxic behavior in medicine.
The cognitive dissonance of knowing what smoking and drinking do while still doing it. 50% of smokers die from it. 76% of license actions are substance-related. Ask your parents what they'd do differently.
Your stipend is rare financial stability — don't waste it. SIPs, emergency funds, working capital, and why understanding money during residency is more important than earning big after it.
Why every medical resident needs to understand SEM. Branch-wise clinical scenarios showing how not knowing this one concept leads to wrong prescriptions, false confidence, and wasted research.
Deep dive into samples vs populations — why you can't study everyone, what sampling really means, and how this one concept underlies every statistical test you'll ever use.
Confounders, mediators, effect modifiers, colliders — the puppet masters behind every spurious association. Learn to see the strings.
A p-value of 0.001 means nothing if the effect is too small to matter. The critical distinction between statistical and clinical significance.
The confusion isn't about the math — it's about the words. Risk is what you HAVE. Odds is what you BET.
The 6 ways medical residents misinterpret p-values, the ASA's 2016 statement, and why Fisher's Lady Tasting Tea started it all.
Parametric vs nonparametric — the naming makes no sense until you understand the one word that explains everything: parameters.
Statistical power — why underpowered studies are the silent killer of good research, and how to calculate sample size properly.
Variance and deviation — the seemingly circular math that actually encodes deep statistical truth about spread, scaling, and additivity.
The level of significance — alpha — is not a law of nature. It's an arbitrary threshold with a fascinating history and real consequences.
The null hypothesis — the most counterintuitive idea in statistics, and the one most students never truly understand.
Confidence intervals — what they actually mean (not what you think), why they're better than p-values, and how to read them clinically.
Correlation — the most abused concept in medicine. Pearson, Spearman, partial, and why "correlation does not imply causation" is only half the story.
Normal, skewed, bimodal, Poisson — understanding distributions is understanding the DNA of your data.
Why parametric tests fail on non-normal data — the mathematical assumptions, what happens when you violate them, and the alternatives.
Linear, logistic, Cox — regression analysis from Galton's peas to modern clinical prediction models.
Type I and Type II errors — the courtroom analogy that makes alpha and beta unforgettable.
Multiplicity — the multiple comparisons problem, Bonferroni correction, and why subgroup analyses are a statistical minefield.
Standard deviation — the most quoted, least understood number in medical research. What it means, when to use it vs SEM, and why n-1.