Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

Bias, use of language, ethics, cost, time and timing, privacy, and cultural sensitivity may influence the collection of data and how that data is displayed. The people who collect the data can manipulate the ways of how they get the results they want. 

Bias influences the collection of data because you can manipulate the words to make one side of data more or less appealing. This causes buyers, users, and customers to be mislead by the wording of the question and make them feel obligated to choose one side.

Example: Tide is trying to get more customers so they put out an advertisement in Costco to gain publicity and increase their sales.

Their advertisement looks like this: Over 90% of Canadians today choose Tide over the leading detergent. Out go these 4 brands, which one would you choose? This is bias because it's leading the customers towards picking Tide as they stated that most Canadians choose Tide.

The same advertisement without bias looks like this: Out of these 4 brands, which one would you choose? This isn't bias because there is no information leading them in any direction; the customers are just stating their opinion.

Use of language influences the collection of data by leaving out certain information and replacing it with a bias word.

Bias example: Most customers pick Campbell’s Chicken Noodle Soup over President’s Choice Chicken Noodle Soup. This is bias because it leaves out the ac-tual percentage of people who choose that soup. This misleads customers because when they hear most, they think of a higher percentage than around 80%.

Un-bias example: 54% of customers pick Campbell’s Chicken Noodle Soup over President’s Choice Chicken Noodle Soup. This isn’t bias because they are telling customers the actual percentage. This shows that just over half of customers choose Campbell’s and a large percent still choose President’s Choice.

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

Ethics influence the collection of data by asking the question in a certain way. A Bad ethic is asking a question in an inappropriate way or crossing the line which could make the customer feel uncomfortable. A good ethic is letting the customer make their own decisions and speak on their own terms.

Unethical: A sales person comes to your door and you say you are not interested. The sales person comes back later that day and continues throughout the week.

Ethical: A sales person calls you and asks question you don’t want to answer. You politely say you're not interested and ask them not to call this number anymore. They say thank you and don't call your number again.

Cost influences the collection of data because it may cost a lot of money to ask large amounts of people. Because of this, smaller percentages of the population will be asked and it effects the data collected. Smaller amounts of people may have similar opinions to each other and their opinions don’t relate to the rest of the population.

Example: Starbucks is asking their customers which new summer drink is their favourite. They asked 10 customers instead of 100 to cut the cost of the questionnaires needed. 8 of 10 people liked the iced coffee, and 2 of 10 people liked the cold brew coffee. Their results concluded that 80% of people asked like the new iced coffee. The customers don’t know how many people were asked for the poll, so they just assume that it’s a larger number than 10. This tricks customers into thinking that the results are 100% true, when in reality very few people were asked and their opinion doesn’t relate to everyone else.

Time and timing influences the collection of data because the time effects what you are more likely to choose. Also based on the month or what time of the year the ques-tions being asked your answer can vary.

Example time of year: Asking someone what there favourite drink from Starbucks is it depends on the month because in winter they most likely will choose a warm drink instead of a cold one.

Example time of day: If someone is selling food either a muffin or steak often your answer will depend on what time it's at in the day.

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

Privacy influences the collection of data because the information stays private and any-one could choose the answers. This makes it inaccurate because other people who view the data wouldn't know how they obtained it.

Example: Confidential surveys where others don't know who took the survey or when the data was collected.

Cultural sensitivity influences the collection of data because if you ask a question to a certain culture they will answer differently than people who are not in that culture. The answers will differ because the people who are cultural will do certain things that the non-cultural people do not, and vice versa.

Example: Asking people from a certain culture that can not eat ham, what brand of ham they would choose. The answers will differ because the people who are not aloud to eat ham means that they don't like a certain brand of ham over another.

The difference between a population and a sample is the amount of people asked. Asking a population means that you asked every person in the area the same question, and then figured out the results with the data collected. A sample means that you asked one or a few people from different parts of the region that you’re collecting data from. The data collected from the entire population will be accurate because you actually asked the whole population. On the other hand, the data collected from a sample won’t be accurate because those few people don’t relate to the entire population’s opinion.

Example: A clothing brand wants to say that their jeans are the most bought throughout all of Canada.

Population: They asked every person in Canada the same question about what jeans they wore the most. Most people said they wore their brand most often, so then the data would be correct and okay to use as a population viewing point. This is population because they asked everyone in that population.

Sample: They asked 100 people from each province/territory and displayed their results as a “population” result. This is a sample because they didn’t ask every single person in that population, only 100 from each section of that population.

Convenience sample means that you ask the first people you see and don’t pay

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

attention to age, gender, size, etc. No matter what the question, the data collected will not be as accurate as it could be. This benefits of this sampling method are that it’s fast, easy, and efficient.

Example: A K-12 school asks students what electives they prefer. They ask the first 100 people who walk through the school front doors. This is a convenience sample because a gr.1 girl will have a completely different opinion then a gr.12 boy.

Random sample means that you ask random people and everyone has an equal opportunity. A random sample may be a little more accurate than a convenience sample because have more control over who you ask and where you are. Similar to the conve-nience sample, the random sample’s benefits are that it’s efficient, fast, and easy.

Example: You’re in a grocery store and you ask a random 50 customers which cereal is their favourite. This is a random sample because you ask people in the store at random.

Stratified samples can influence the collection of data because each group of people are separated into stratas. Each stratum will give one sample from a random person which could make the data more accurate because you get balanced and reasonable answers.

Example: Organize groups of volleyball players and get a certain amount from each group to choose what type of nets they should put up.

Systematic samples influence the collection of data because they collect data by a list or pattern of people and randomly pick people. These results will have a very wide range in answers because nobody is influencing a certain answer.

Example: Every 4th person in the Tim Hortons line is asked how they feel about the service there.

Voluntary sample means that it is made up of people who self-select into the survey. These people voluntarily give their opinions and do not have to give any data if they do not want to.

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

Example: A TV show asks viewers to participate in giving their opinion by going to a website and clicking their favourite character. This is a voluntary sample because the viewers are asked to give their opinion if they want to.

Choosing an inappropriate sampling method may bias the data because it depends on the sample procedure, size, and the participation(the response). It all depends on who you ask and where you ask it. Sampling methods may become bias when it favours the outcome of others, depending on the situation.

Examples: -You randomly ask if people like movies, right in the entrance of a movie store. Thelocation of the poll effects the answer since people will be going in and out of a movie store in the first place.

-There is a gr.10 art class at 10:45am. You ask the first 10 people you see entering the class if they like the movie My Life as a Book or 22 Jump Street better. This is bias because you are asking a gr.10 class if they like a kids movie better or a teen movie better.

Theoretical probability is what you expect to happen, but it isn’t always what actually happens. You make an educated guess or prediction depending on what you know about the subject matter. Experimental probability is what actually happens instead of what you were expecting to happen. This means that you actually do something to find out the data and record it.

Example: You put a red and blue stone in a bag. They are both the same shape, size, and texture. The only thing different is the colour, which you cannot see from the out-side of the bag. You pull out 1 stone, record the result, then put it back and restart. You do this 100 times.

Theoretical: You assume that since their are only 2 stones, half the time it will be blue and the other half it will be red. This is theoretical probability because it’s what you would expect to happen.

Experimental: You do the experiment and pull out 1 stone, record the result, then put it back and restart for a total of 100 times. The results you got were that you pulled out a blue stone 54 times and a red stone 46 times. It’s very close to your theoretical result, but still not the same. This is experimental probability because you ac-tually do the experiment to collect the data.

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

3 examples of misleading statistics used in the media:

Kellogg’s had to discontinue this advertisement because their statistics were dubious according to the Federal Trade Commission. Earlier that same year they were forced to discontinue their Frosted Mini Wheats. Their ad claimed that “the cereal was clinically shown to improve kid’s attentiveness by nearly 20%”. The FTC found that only half the kids who ate the cereal improved attentiveness and that only 1 in 9 improved to 20%

more attentive. This is a misleading ad because Kellogg’s left out the whole information, yet their ad is still true. They purposefully left out how many kids actually reached 20%, so more customers would be tricked into thinking their kids are improving their attentiveness.

Vitamin Water’s advertising is very misleading because of their word choices and infor-mation they very carefully left out. Vitamin Water is a mineral and vitamin enhanced drink that is marketed to make you believe they are healthy. Vitamin water contains many minerals, vitamins, but also tons of sugar. The drink has so many vitamins and minerals in it that our body ca’t use all of it. In the end we pee out most of the added supplements into the drink because our body has no use for them. Instead, we are left

with all the sugars just like any other sugary drink. This is a misleading claim be-cause Vitamin Water left out cer-tain pieces of information, yet their product’s claims are still true.

Makenna Chow Statistics Partner 09/06/16Sarah Lacusta Project

Maybelline put out this ad for their mascara and made the claim that its “America’s favourite mascara”. This is an example of a population statistic because they couldn’t have asked all of America what their favourite mascara was. Also, a lot of men don’t wear mascara, so how could it be all of America’s favourite mascara.This is a misleading claims because Maybe-linne couldn’t have asked every person in America what their favourite mascara is; and not every person in America wears mascara.