1. By: Raiyah and Adrienne CHAPTER: 11 STATISTICS

2.  If a company is bias towards their product it’ll make the consumers want to buy their product over any other competitor.  Using bias language can influence the decision of a costumer. If a costumer is deciding between multiple products and one competitor uses bias language it gives them an edge on other competitors because it convinces the costumer to buy their product.  Using specific language can change the way a question is received by the costumers which with also influence their decision on which product to buy. BIAS AND USE OF LANGUAGE

3.  The cost of a product can influence a decision by the costumer if the cost is lower than a competitors.  Distributers use specific language before the price of their product to make it seem like there’s a deal when really there isn’t. Ex. “Price as low as” or “for a limited time only.” COST

4.  If a distributer is trying to sell a product or get a statistic for which product is best they use timing wisely to get an accurate response or a response that they want.  For example: if a distributer wants a statistic on which brand of school supplies has the most sales, they would do they’re research in August and September, not in March or April when school is almost over and no one is buying. TIME AND TIMING

5.  If a creator of the survey wants to ask people their opinion on a certain topic but not all of the people who were asked want to respond so that can throw off a statistic.  Example: The governement wants to do a survey to see how many people are voting for Donald Trump so they ask 50 people who they are voting for. Only 25 respond saying that they are voting for Donald but they other half don’t give a response because they don’t feel comfortable revealing that information. So unless the governement states that only 25 people responded the statistic gives off an incorrect impression. PRIVACY

6.  Sometimes peoples religions can help influence a decision, because if you are using bias opinion to hint towards an answer that is religious, lets say related to god, and the other answer is related to the devil, they will chose the one with god! Sometimes questions can offend certain religions, such as 90% of people agree that Christians are the most believable. Also the data collectors can ask certain religions for a certain result. CULTURAL SENSITIVITY

7.  In statistics populations is a word used to describe the entire overall group you are trying to receive information from. Sample is the actual amount of people or things you received information from. Sample is used to represent the entire population.  Example: when you receive a sample product, it is a sample because it is only a small portion used to represent and show what the rest of the product is like. Same thing in statistics, the sample you received data from, is supposed to represent what the entire population thinks.  Example 2: if you are trying to find how many dentists recommend a brand, you want to know from all the dentists (the population), but if you don't have the funds or the ability to do so, you can ask a portion of dentists ( a sample) their opinion, and use that to get an estimate of what the whole population thinks POPULATION VS. SAMPLE

8.  CONVENIENCE SAMPLE: Is sampling done in the most convenient way to the researcher, it’s sampling the group with the easiest access to them. Ex. If the researcher wants the opinion of the population of teachers they would ask the teachers at the school closest to them.  RANDOM SAMPLE: Is sampling done by choosing random subjects to be a part of the sample. Anyone that qualifies has the same amount of chance to be chosen. Ex. If you want to know what food the cafeteria should serve at lunch you could ask any student at random and it wouldn’t matter.  STRATIFIED SAMPLE: Is sampling targeting specific groups of people instead of cross sections. Ex. If the group was children under the age of 7 and researchers asked what is their favorite hobby the result may be coloring, but that’s only because they asked a specific group if people to get a specific response.  SYSTEMATIC SAMPLE : Is sampling that allows you to include more people in the testing but only surveying a systematically chosen group or number of people. Ex. There is a survey of 1000 people except you only use the data from every 10 th person.  VOLUNTARY RESPONSE SAMPLE: Is sampling where the samplers volunteer to be a part of the poll. Ex. A news show asks viewers to participate in an on-line poll. The sample is chosen by the viewers, not by the people surveying. SAMPLING METHODS

9.  If a distributer uses an inappropriate method of sampling it could throw off the message the distributer is trying to convey to the consumers.  Example #1: If there was a survey to see which fast food restaurant was the most popular but they used a stratified sample of people who work at McDonald’s to get a certain response.  Example #2: If the survey was to see which subject in school was the most popular but the distributer uses voluntary sampling and none of the volunteers are in school so the distributer won’t get an accurate response. INAPPROPRIATE SAMPLING METHODS

10.  An example is when you flip a coin ten times. Theoretically you expect the coin to long on each side 5 times. Because there is a 50 50 chance so the theoretical statistic is 50% on each side. Experiments is the actual outcome of the test based on the amount of times you flipped the coin. What is supposed to happen VS what actually does happen EXPERIMENTAL VS. THEORETICAL

11. EXAMPLE #1 WHAT’S WRONG WITH THIS STATISTIC? 1.The chart makes Chevy seem like the obvious option to choose when in reality if you pay attention Chevy is only 1% higher than Ford and 3% more than Nissan but the chart makes it look a lot higher. 2.We also don’t know if this statistic is for Chevys all around the world or just from one dealership. 3.We don’t know how popular Chevy was 10 years ago or how many they sold, also they could have had more repairs and maintenance done on them, the quality could be horrible but the car could still run, they never said the cars run smoothly.

12. EXAMPLE #2 WHAT’S WRONG WITH THIS STATISTIC? 1.This is an example of a population statistic, obviously Maybelline couldn’t have asked all of America which mascara they preferred, so that is misleading. 2.We don’t know what the question Maybelline asked to get these results. They could’ve listed multiple mascaras and made the consumers choose which one they preferred.

13. EXAMPLE #3 WHAT’S WRONG WITH THIS STATISTIC? 1.We don’t know what the original question was. Based on the information showed we can assume there were only two options to the question, either have homework or go to school. 2.In the pie chart it says “do homework” but in the question it’s self it says do homework on Saturdays so by adding “on Saturday” it makes the question more bias and is favouriting a certain answer.