Sample and Survey Recap!

Population The entire thing you are testing (ex: everyone in a high school, all of the water in a pool.)

Sample A sample is a part the population. You get samples so that you can make predictions of the entire population. (Ex: a gallon of water from a pool, 50 people from your church, etc.) It is taking a piece of data from a larger population.

Types of Sampling: Simple Random The fairest type of sampling method. Everyone and everything has an equally likely chance of being chosen Ex: Drawing names out of a hat

Systematic Random Sampling When a sample is chosen by choosing every nth item or using a time interval Ex: Selecting every 10th cake can Selecting a person every 20 seconds. (Randomly choose n, stick with the pattern)

Stratified Random Sampling When a sample is chosen by dividing the population in a fair way, then randomly choosing the same number of items in each divided section. Ex: FIRST separating a high school by boys and girls, and THEN selecting 50 boys and 50 girls randomly.

Convenient Sampling When you chose a sample because it’s “easy.” Ex: Choosing the first 10 papers in a stack Picking the first 10 people that walk through the door Choosing your friends to fill out a survey

Voluntary Sampling A person chooses to be part of the sample Ex: You call in to vote for “American Idol” You put a suggestion box at the store and if someone want to fill it out, they can You submit answers to “Family Feud”

Biased Sampling Biased sampling is “unfair” in some way Ex: You ask 100 people from Jamaica if they enjoy the snow during winter...it doesn’t snow in Jamaica, so how can they like it???? That would give data that is not possibly fair….

Other samples that are biased ● You go to an organic farm and ask the customers if they like fresh produce ● You want to see if high school students enjoy ice cream, so you take your survey at the ice cream shop. o If they are at the ice cream shop...wouldn’t they already enjoy ice cream?????

Unbiased Sampling These are fair ways of getting a sample: The three “S”s: Simple Random Systematic Random Stratified Random

What type of Sampling? You pick every 4th person to walk through the door Systematic: picking every 4th person is systematic

You take the top 20 papers in a stack to review Convenient: This is too easy too do and leads to UNDER-REPRESENTATION in the other papers

The store manager puts a suggestion box by the register Voluntary: You don’t have to put in a suggestion at all

You put everyone’s name into a jar and selects 12. Simple Random Sampling: Everyone is being represented and can be chosen equally

Biased vs Unbiased John is doing a survey and asked 20 of his friends to fill it out. Biased because it is convenient and everyone that is not John’s friend isn’t being represented

Janet wants to select students from her class, so she enters all their names into a random generator. Unbiased because everyone has the same chance of having their name chosen

None of us are perfect…. So we give ourselves some wiggle room and call it: Margin of Error 1 √n ∓ n is your sample size ()( 100% )

Why do we have Margin of Error? It allows for human error either by the ● surveyor ● surveyee

For instance,... If I take a survey, my numbers might have to be rounded, accounting for a slight misrepresentation. Also, if a survey asked for your weight, you may not give accurate information….you may fudge that number a little bit.

Example In a sample of 75 teenagers, 37 enjoyed reading on the weekends. What is the margin of error for this data? Percentage of readers: Margin of Error: 37 ∓ 1 75 √75 = 49.3% = ∓ 11.5%

Just FYI…. The larger the sample size, the smaller the margin of error. Easy breezey...lemon squeezy!?!?!