To do this project, you should understand the following terms and concepts (do background research to fill in any gaps in your knowledge): 1  binary (base 2), 2  decimal (base 10), 3  hexadecimal (base 16), 4  bit, byte, kilobyte (kB), megabtye (MB), 5  radix (number base).
Sampling distribution. In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given statistic based on a random sample. Sampling distributions are important in statistics because they provide a major simplification en route to statistical inference.
BREAKING DOWN 'Sampling'. When taking a sample from a larger population, it is important to consider how the sample will be drawn. To get a representative sample, the sample must be drawn randomly and encompass the entire population. For example, a lottery system could be used to determine the average age of students in a University by sampling 10% of the student body, taking an equal number of students from each faculty.