Final answer:
Nonparametric tests are referred to as distribution-free tests. They don't assume normal distribution and are useful for small samples or non-numeric data. A parameter describes a whole population, while a statistic comes from a sample.
Step-by-step explanation:
Nonparametric tests are referred to as distribution-free tests. These tests do not require the assumption of normality in the data. They are used when the sample size is too small to determine if the population has a normal distribution or when the data are ordinal or nominal. An example of a nonparametric test is the Mann-Whitney U test for comparing two independent groups. The concept of parameters is different from nonparametric tests; a parameter is a numerical characteristic of a population, versus a statistic which is a characteristic of a sample. An example of a situation where you would calculate a parameter rather than a statistic is when using data from an entire population, as any calculated average or percentage would describe that population (a parameter) rather than a sample from that population (a statistic).
Referring to the provided multiple-choice questions noted in the student's question, if the U.S. federal government conducts a survey of high school seniors regarding their future education plans and 50% say they are planning to attend a four-year college, that 50% is a statistic because it's derived from a sample of the population, not the entire population.