Final answer:
Randomization in sampling is achieved using random number generators to select unbiased samples, applicable in stratified or systematic sampling methods.
Step-by-step explanation:
When carrying out a design for randomization, a common approach is to use a random number generator to ensure an unbiased selection. For example, if Lisa needs to select three class members randomly, she may use a table of random numbers, a calculator's random number function, or a computer program. With the generated numbers such as .94360, .99832, .14669, .51470, .40581, .73381, .04399, Lisa will read two-digit groups until she has selected the required number of class members. For instance, she would read the number .94360 as the two-digit groups 94, 43, 36, 60 and correspond these to student ID numbers if applicable.
To ensure a stratified random sample, one could divide the data into strata, such as differentiated by lap times in a racing dataset. Then, using the random number generator, select a predetermined number of entries from each stratum to form a well-balanced sample.
For systematic sampling, like choosing 12 quiz scores from a set, you could use a command such as randInt(1, 60) on a calculator, which gives a random integer between the numbers specified. The first number is recorded, and then subsequent numbers are selected in a systematic way, for example, every tenth score from the first random number.