This project aims to help students understand and master the different sampling methods and be able to identify which one is being used depending on the scenario given.
Credit: Soumidata:image/png;base64,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:image/png;base64,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1stratified random sample1cluster sample1censusendfalseEach student at a school has a student identification number. Counselors have a computer generate 50 random identification numbers and those students are asked to take a survey.,simple random sample,"This is simple random sample because each student has an equal chance of being selected to be a part of the sample, ensured by the computer generator.""An airline company wants to survey its customers one day, so they randomly select 5 flights that day and survey every passenger on those flights.",cluster sample,"This is a cluster sample because each flight represents a cluster, which can be a considered a subset of the population. By surveying every passenger on the selected flights, you will end up with a final sample that represents the total population.""32 sophomores, 35 juniors and 49 seniors are randomly selected from 230 sophomores, 280
juniors, 577 seniors at a certain high school. ",stratified random sample,"This is a stratified random sample because all students are split into common groups/strata, based on grade-level. A random sample is taken of each to make sure all grade-levels are represented in the final sample.""Molly is interested in what kinds of activities are enjoyed most by third graders. She makes a list of all of the third graders in her school district, alphabetizes the list and asks every 10th student what they like to do for fun. What method of sampling is this?",systematic random sample,"This is a systematic random sample because participants in the sample are chosen solely based off their position (ex: 2nd in line), and is determined by the interval.""A traffic cop was stopping every 5th car to check for insurance and license. What type of sampling
method is the cop using?",systematic random sample,This is a systematic random sample because the chance for a car to be stopped and checked was only determined by the interval."To conduct a pre-election opinion poll on a proposed amendment to the state
constitution, a random sample of 10 telephone prefixes (first three digits of the
phone number was selected and all households from the phone prefixes selected
were called.",cluster sample,"In a cluster sample, a random selection of clusters are made and every member of each selected cluster is included. In this case, selected clusters are the sample of 10 phone prefixes, in which all households with that prefix are called.""Subscribers to the magazine Sound Alive were assigned numbers. Then a sample
of 30 subscribers was selected by using a random-number table. The subscribers
in the sample were invited to rate new headphones for a “What do Subscribers
Think” column",simple random sample,"This is a simple random sample because every subscriber had an equal chance to be chosen, which was determined by the computer (no bias).""To judge the appeal of a proposed television sitcom, a random sample of 10
people from each of three different age categories was selected and those chosen
were asked to rate a pilot show.",stratified random sample,This is a stratified random sample because a random sample of 10 people from each strata (each age group) was taken to represent all aspects of the population in relation to age."The names of 70 contestants are written on 70 cards, The cards are placed in a bag, and three
names are picked from the bag.",simple random sample,"This is a simple random sample because every contestant had an equal chance to be selected as a winner. Even if it was a low chance, it was still the same amongst all.""A pregnancy study in Chicago, randomly selected 25 communities from the metropolitan area,
then interviewed all pregnant women in these communities.",cluster sample,This is a cluster sample because only the randomly selected communities were completely surveyed; not any of the other communities/clusters.A random number generator is used to choose fifteen classes. Then a college student collects data from each person in these classes.,cluster sample,"This is a cluster sample because each class represent a subset of the population. Therefore, randomly surveying the chosen clusters is similar to taking a simple random sample of the entire population."Every US resident was surveyed to collect information about their opinion on the new law.,census,"This is a census because the entire population is considered as the ""sample."""To maintain quality control in a brewery every 20th bottle of beer coming off the production line was opened and tested,systematic random sample,This is a systematic random sample because the chance for a bottle to be opened and tested was only determined by the interval.To judge the appeal of proposed television sitcom a random sample of 10 people from each of three different categories was selected and those chosen were asked to rate a pilot shot,stratified random sample,This is a stratified random sample because a random sample was taken from EACH category to participate in the study.-42920104000-10