Hypothesis Testing

Research questions and hypotheses?

May 20, 2020

When doing proposals or client reports, we often refer to “research questions” and “research hypotheses” (sometimes used interchangeably). What is the difference?   Research Questions do NOT entail specific predictions (magnitude or direction of the outcome variable) and are therefore phrased in a question format that could include questions about descriptives, difference or association (or relationship). These assist the researcher to choose the most appropriate statistical techniques. Let’s look at each:   1. Research questions that relate to describing [READ MORE]

Statistical Power Analysis

April 27, 2020

(Statistical) Power Analysis refers to the ability of a statistical test to detect an effect of a certain size if the effect really exists. In other words, power is the probability of correctly rejecting the null hypothesis when it should be rejected. So while statistical significance deals with Type I (α) errors (false positives), power analysis deals with Type II (β) errors (false negatives), which means power is 1- β Cohen (1988) recommends that research studies be designed to achieve alpha levels of at least .05 and if we use Cohen’s rule of .2 for β, then 1- β= 0.8 (an 80% chance [READ MORE]

Measuring effect size and statistical power analysis

March 3, 2020

Effect size measures are crucial to establish practical significance, in addition to statistical significance. Please read the post “Tests of Significant are dangerous and can be very misleading” to better appreciate the importance of practical significance. Normally we only consider differences and associations from a statistical significance point of view and report at what level e.g. p<.001 we reject the null hypothesis (H0) and accept that there is a difference or association (note that we can never “accept the alternative hypothesis (H1)” – see the [READ MORE]

Type I and II errors – Hypothesis testing

February 10, 2020

In so many statistical procedures we execute, the statistical significance of findings is the basis of statements, conclusions, and for making important decisions. While the importance of statistical significance (compared with practical significance) should never be overestimated, it is important to understand how statistical significance relates to hypothesis testing. A hypothesis statement is designed to either be disproven or failed to be disproven. (Note that a hypothesis can be disproven (or failed to be disproven), but can not be proven to be true). Hypotheses relate to either [READ MORE]

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