Statistics doesn’t have a Magna Carta, constitution, or bill of rights to enumerate laws, guiding principles, or limits of power. There have been attempts to articulate credos for statistical practice. Two of the most enduring ones are based on the work by Robert P. Abelson, a former statistical professor at Yale. If Abelson wasn’t the
When learning statistics, you’ll encounter many formulas based on principles of probability and mathematics. But statistics isn’t just a formulaic process where you enter data and are told what to do. Statistics should guide, not dictate, decisions. In making decisions, though, there are different styles of interpreting data. Although a lot of people think statistics
If you know even just a little about statistics, you know that the value .05 is special. When the p-value obtained from conducting a statistical test falls below .05, it typically gets a special designation we call statistically significant. This is the conventional threshold for publishing findings in academic journals, and consequently, it is ascribed
Hypothesis testing is one of the most common frameworks for making decisions with data in both scientific and industrial contexts. But this statistical framework, formally called Null Hypothesis Statistics Testing (NHST), can be confusing (and controversial). In an earlier article, we showed how to use the core framework of statistical hypothesis testing: you start with
Statistically significant. p-value. Hypothesis. These terms are not only commonly used in statistics but also have made their way into the vernacular. Making sense of most scientific publications, which can have practical, significant effects on public policy and your life, means understanding a core framework with which we derive much knowledge. That framework is called
When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed
