Contents
How do you test whether a data sample is normal or not?
An informal approach to testing normality is to compare a histogram of the sample data to a normal probability curve. The empirical distribution of the data (the histogram) should be bell-shaped and resemble the normal distribution. This might be difficult to see if the sample is small.
How do you know if a sample size normality assumption is satisfied?
Q-Q plot: Most researchers use Q-Q plots to test the assumption of normality. In this method, observed value and expected value are plotted on a graph. If the plotted value vary more from a straight line, then the data is not normally distributed. Otherwise data will be normally distributed.
What is the t-test to use if one is comparing groups of non normal data?
In case of non normal distribution, to compare two independent groups, Mann Whitney U test is appropriate.
How to test for differences between sample data?
Sometimes we will have too few data points in a sample to do a meaningful randomization test, also randomization takes more time than doing a t-test. This is a test that depends on the t distribution. The line of thought follows from the CLT and we can show differences in means are t distributed.
How to test a difference in two population means?
Step 1: Determine the hypotheses. The hypotheses for a difference in two population means are similar to those for a difference in two population proportions. The null hypothesis, H 0, is again a statement of “no effect” or “no difference.” The alternative hypothesis, H a, can be any one of the following.
How to test the hypothesized difference in means?
Test method. Use the two-sample t-test to determine whether the difference between means found in the sample is significantly different from the hypothesized difference between means. Using sample data, find the standard error, degrees of freedom, test statistic, and the P-value associated with the test statistic.
How to test for differences in gene expression?
We are first simulating two samples from two different distributions. These would be equivalent to gene expression measurements obtained under different conditions. Then, we calculate the differences in the means and do the randomization procedure to get a null distribution when we assume there is no difference between samples, H 0 H 0.