If you were performing inferential statistics on a small set of numerical data then the best test to use would be

The best test to use when performing inferential statistics on a small set of numerical data would depend on the specific characteristics of your data and the nature of your research question. However, generally speaking, the t-test is often a good choice for small sample sizes.

Here are the steps to perform a t-test:

1. Formulate your null and alternative hypotheses: The null hypothesis typically states that there is no effect or difference in the population. The alternative hypothesis is what you might believe to be true or hope to prove true.

2. Calculate the sample mean and standard deviation: The sample mean is the average of your data, and the standard deviation measures the amount of variation in your data.

3. Calculate the t-statistic: The t-statistic is a ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. It's calculated as (sample mean - population mean) / (sample standard deviation/sqrt(sample size)).

4. Determine the degrees of freedom: The degrees of freedom for a t-test is typically the sample size minus 1.

5. Find the critical t-value: The critical t-value is a threshold that your t-statistic must exceed in order to reject the null hypothesis. It's determined by your chosen significance level (commonly 0.05) and your degrees of freedom.

6. Compare