Contents
What are the criteria for model selection?
Below is a list of criteria for model selection. The most commonly used criteria are (i) the Akaike information criterion and (ii) the Bayes factor and/or the Bayesian information criterion (which to some extent approximates the Bayes factor), see Stoica & Selen (2004) for a review.
Is a t-test a model?
Most of the common statistical models (t-test, correlation, ANOVA; chi-square, etc.) are special cases of linear models or a very close approximation. This beautiful simplicity means that there is less to learn.
What to consider when choosing a t test?
When choosing a t-test, you will need to consider two things: whether the groups being compared come from a single population or two different populations, and whether you want to test the difference in a specific direction. One-sample, two-sample, or paired t-test?
How is the t value of a t test calculated?
A t-test measures the difference in group means divided by the pooled standard error of the two group means. In this way, it calculates a number (the t-value) illustrating the magnitude of the difference between the two group means being compared, and estimates the likelihood that this difference exists purely by chance (p-value).
What are assumptions made when conducting a t-test?
The assumption for a t-test is that the scale of measurement applied to the data collected follows a continuous or ordinal scale, such as the scores for an IQ test. The second assumption made is that of a simple random sample, that the data is collected from a representative, randomly selected portion of the total population.
Which is the null hypothesis in a t test?
You can test the difference between these two groups using a t-test. The null hypothesis (H 0) is that the true difference between these group means is zero. The alternate hypothesis (H a) is that the true difference is different from zero. What type of t-test should I use?