When do we use t test?when nominal data is givenWhen n is greater than or equal to 30When ordinal data is givenWhen n < 30

We use a t-test in statistics to compare the means of two groups and determine if there is a significant difference between them. This test is used when the sample size (n) is less than 30 and when the data is interval or ratio level (continuous).

So, among the options you provided, we use a t-test when n < 30.

We do not use a t-test for nominal or ordinal data because these types of data do not provide information about differences in means. Nominal data is categorical and does not have a numerical value or order, while ordinal data has a clear order but the differences between the values are not consistent.

Also, when n is greater than or equal to 30, we usually use a z-test instead of a t-test. This is because as the sample size increases, the t-distribution approaches the standard normal distribution (z-distribution), and the z-test is more appropriate for larger sample sizes.

We use a t-test in statistics to compare the means of two groups and determine if they are significantly different from each other. This test is used when the following conditions are met:

1. The data is continuous: The t-test is used for continuous data, not for nominal or ordinal data. Continuous data is numerical data that can be measured on a scale and can have almost any numeric value.

2. The sample size is small: The t-test is typically used when the sample size (n) is less than 30. If the sample size is 30 or more, a z-test might be more appropriate.

3. The data is normally distributed: The t-test assumes that the data follows a normal distribution. If this assumption is not met, the results of the t-test may not be valid.

So, to answer your question, we use a t-test when we have continuous data, a small sample size (n<30), and the data is normally distributed.