How do you compare data with different sample sizes?
One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set.
What is an unequal t-test?
In statistics, Welch’s t-test, or unequal variances t-test, is a two-sample location test which is used to test the hypothesis that two populations have equal means.
Can you do Anova with unequal sample sizes?
You can perform one way ANOVA with unequal sample sizes. You must consider the assumptions of Normality, equality of variance and independence ( that mentioned by Saigopal ) before using ANOVA and in a case of not correct assumption then you must use non-parametric test ( Kruskal-Wallis test ).
Can ANOVA be used for unequal sample sizes?
Can Anova be used for unequal sample sizes?
How do you calculate t test?
Sample question: Calculate a paired t test by hand for the following data: Step 1: Subtract each Y score from each X score. Step 2: Add up all of the values from Step 1. Step 3: Square the differences from Step 1. Step 4: Add up all of the squared differences from Step 3. Step 5: Use the following formula to calculate the t-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).
What is the formula for single sample t test?
The correct formula for the upper bound of a confidence interval for a single-sample t test is: Mupper = t(sM) + Msample. The correct formula for effect size using Cohen’s d for a single-sample t test is: d = (M – μ)/s.
What assumptions are made when conducting a 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.