The meat department manager at a grocery store is worried some of the packages of ground beef labeled as having one pound of meat may be under-filled. He decides to take a sample of 5 packages from a shipment containing 100 packages of ground beef. The packages were numbered as they were put in the box, so each one has a different number between 1 and 100.[br][br]Describe how the manager can select a fair sample of 5 packages.

[size=100]Select [b]all[/b] the reasons why random samples are preferred over other methods of getting a sample.[/size]

[size=150][size=100]Jada is using a computer’s random number generator to produce 6 random whole numbers between 1 and 100 so she can use a random sample. The computer produces the numbers: 1, 2, 3, 4, 5, and 6. [/size][/size]Should she use these numbers or have the computer generate a new set of random numbers? Explain your reasoning.

[size=150][size=100]A group of 100 people is divided into 5 groups with 20 people in each. One person’s name is chosen, and everyone in their group wins a prize. Noah simulates this situation by writing 100 different names on papers and putting them in a bag, then drawing one out. Kiran suggests there is a way to do it with fewer paper slips. [/size][/size]Explain a method that would simulate this situation with fewer than 100 slips of paper.

[size=150][size=100]Data collected from a survey of American teenagers aged 13 to 17 was used to estimate that 29% of teens believe in ghosts. This estimate was based on data from 510 American teenagers. [/size][/size]What is the population that people carrying out the survey were interested in?

A computer simulates flipping a coin 100 times, then counts the longest string of heads in a row[br][img]data:image/png;base64,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[/img][br][br]Based on these results, estimate the probability that there will be at least 15 heads in a row.