What Is Grounded Theory? Imagine you are studying how first-year PhD students cope with the pressure of their new life. You have no hypothesis to test and no ready-made theory that quite fits what you are seeing. Instead of forcing your data into someone else’s framework, you collect interviews, look closely at what people actually …
If you have ever read a thesis that tries to capture what an experience feels like — what it is like to be a first-year nurse, or a patient waiting for a diagnosis — you have probably met phenomenology in qualitative research, even if nobody ever explained the word to you. Phenomenology is a research …
What Is a Pilot Study? A pilot study in research is a small-scale trial run of your study, carried out before the main data collection begins. Think of it as a rehearsal: you test your questionnaire, your interview guide, your procedures, and your analysis plan on a small group of participants so you can find …
If you have ever opened a statistics textbook, looked at the long list of tests — t-test, ANOVA, Mann-Whitney U, Kruskal-Wallis — and thought, “Which one is mine?”, you are not alone. Nearly every student reaches this fork in the road, and the question behind it is the one this guide answers: parametric vs nonparametric …
What Is a Null Hypothesis? A null hypothesis is a statement that there is no effect, no difference, or no relationship between the variables in a study. It is the default position researchers start from — the assumption that nothing interesting is happening — until the evidence proves otherwise. Think of it this way. Suppose …
What Is P-Hacking? Imagine you run a study and your result comes back with a p-value of 0.08 — just above the usual 0.05 cut-off for “statistical significance.” Nothing is significant, and your hypothesis looks unsupported. So you try a few things: you drop a couple of unusual responses, you test a different subgroup, you …
Every hypothesis test is a decision — and like every decision, it can be wrong in two different ways. Understanding type 1 and type 2 errors matters because these two mistakes shape how you design a study, choose your sample size, and interpret a p-value. The idea is simple once you see it clearly. A …
You have just run your analysis, and there it is: p = .03. Significant! Time to celebrate? Not so fast. That p-value tells you your result is unlikely to be pure chance — but almost nothing about whether the difference actually matters. A tiny, meaningless difference can be “significant” with a big enough sample, and …
What Is Power Analysis? Imagine you run a study comparing two teaching methods. You collect data from 20 students, run your test, and get a non-significant result. Does that mean the two methods work equally well? Not necessarily — your study may simply have been too small to detect a real difference. This is the …
Imagine you want to study freelance graphic designers who work entirely online, or people who have left a particular profession, or members of a small community that has no public list anywhere. You cannot draw names from a list, because no list exists. So how do you find your participants? One answer is snowball sampling: …