What are the 3 types of t-tests?

There are three types of t-tests we can perform based on the data at hand:

• One sample t-test.
• Independent two-sample t-test.
• Paired sample t-test.

What are two main assumptions underlying the repeated-measures t-test?

The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.

Why do we use paired t-test?

A paired t-test is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. Since we are ultimately concerned with the difference between two measures in one sample, the paired t-test reduces to the one sample t-test.

What is the difference between paired t test and repeated measures Anova?

Repeated Measures ANOVA (RMA) is the extension of the paired t test. RMA is also referred to as within-subjects ANOVA or ANOVA for paired samples. (In paired samples t test, compared the means between two dependent groups, whereas in RMA, compared the means between three or more dependent groups).

Why use a repeated measures Anova?

The benefits of repeated measures designs are that they reduce the error variance. This is because for these tests the within group variability is restricted to measuring differences between an individual’s responses between time points, not differences between individuals.

What is t-test used for?

A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics.

Which kind of t-test should be used?

A t-test can only be used when comparing the means of two groups (a.k.a. pairwise comparison). If you want to compare more than two groups, or if you want to do multiple pairwise comparisons, use an ANOVA test or a post-hoc test.

What is a repeated measures t-test used for?

The t-test assesses whether the mean scores from two experimental conditions are statistically different from one another. A repeated-measures t-test (also known by other names such as the ‘paired samples’ or ‘related’ t-test) is what you should use in situations when your design is within participants.

Why is a paired t-test more powerful?

Paired t-tests are considered more powerful than unpaired t-tests because using the same participants or item eliminates variation between the samples that could be caused by anything other than what’s being tested.

What is the t-test used for?

Why is repeated measures ANOVA more powerful?

More statistical power: Repeated measures designs can be very powerful because they control for factors that cause variability between subjects. Fewer subjects: Thanks to the greater statistical power, a repeated measures design can use fewer subjects to detect a desired effect size.

When to use T vs Z test?

T-score vs. z-score: When to use a t score. The general rule of thumb for when to use a t score is when your sample: Has an unknown population standard deviation. You must know the standard deviation of the population and your sample size should be above 30 in order for you to be able to use the z-score.

When to use a paired t test?

The paired t-test is used when the variable is numerical in nature (for example, the height of a person or the weight of a person) and the individuals in the sample are either paired up in some way (such as a husband and wife) or the same people are used twice (for example, preprocedure and postprocedure).

Is t test similar to Z test?

When T-test is used in large samples, the t-test becomes very similar to the Z-test. There are fluctuations that may occur in T-tests sample variances that do not exist in Z-tests. Because of this, there are differences in both test results.

How do you write a t test?

For each type of t-test you do, one should always report the t-statistic, df, and p-value, regardless of whether the p-value is statistically significant (< 0.05). A succinct notation, including which type of test was done, is: one-sample t(df) = t-value, p = p-value. or. two-sample t(df) = t-value, p = p-value.