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 …
If you have ever filled in a questionnaire that asked you to rate statements from “Strongly disagree” to “Strongly agree”, you have already used a Likert scale. It is the most common way researchers measure attitudes, opinions and perceptions — and yet it is also one of the most commonly mishandled parts of a student …
Introduction If you have ever sat in front of twenty open PDFs, a half-written literature review, and the sinking feeling that you have read everything but remember nothing, you are not alone. That is the moment most students discover they needed a system — not more reading. A literature review matrix is that system. It …
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 …
Imagine you run a survey to find out whether students like your university’s new online library. You ask: “Don’t you agree that the new online library is a huge improvement?” Most people say yes — and you walk away thinking the library is a hit. But did you actually measure their opinion, or did your …
If your supervisor has just asked you for an annotated bibliography and your first thought was, “Is that just a reference list with extra steps?” — you are not alone, and you are not far wrong. An annotated bibliography is a list of sources, but each entry carries a short paragraph that shows you actually …
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 …
A questionnaire looks simple. A list of questions, a link, a few hundred responses — and then the trouble starts. Answers that contradict each other, half-finished surveys, and results you cannot quite trust. In most cases, the problem is not the sample size or the statistics. It is the questionnaire itself. Good questionnaire design is …
What Is a Meta-Analysis, and Why Does It Matter? If you have ever read five studies on the same question and found five different answers, you already understand the problem a meta-analysis solves. One study says an intervention works brilliantly. Another finds almost no effect. A third sits somewhere in between. Which one should you …
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: …