Influence on the Collection of Data:
Bias: Being prejudice in favor or against one thing, usually considered to be unfair.
Example: A survey asks “would you stop using paper for a day to save an endangered species ?” this question creates a feeling that by saying no you are saying you wouldn’t want to save the animals, so more people are going to say yes.
Time and Timing: Using a certain event to dictate a certain result in favor of what you want.
Example: A phone company makes a commercial about their monthly deals and airs it during the superbowl when a lot of people are watching, they want more people to see their deals so they air it when most people are watching.
Use of Language: Using words to influence the result a person might pick.
Example: A survey says “most people prefer coffee over tea, what is your opinion?”
Ethics: Using a persons morals or principles to persuade them into picking a result.
Example: “Pick yes to save the red pandas !” this would be an example of ethics because your using a peoples emotions to influence the amount of people that choose yes.
Privacy: Can be used to persuade people to answer in a certain way, if they know that their response isn’t going to be kept private they may answer differently then they would have if their response was kept private.
Example: A online survey asks for your phone number and address before starting the survey, if you know this before hand you may lie about your answers just incase the results become public.
Cultural Sensitivity: The person conducting the survey should be aware of cultural preferences and their opinions because this can effect the data, if the person conducting the survey isn’t careful they could offend other cultures altering the data they might get.
Example: If a meat shop went to a Muslim church and ask them what their favourite type of pork was, this would be offensive because they don’t eat pork.
Cost: May influence the data because it may cost more to conduct the survey itself than the benefits that come with it.
Example: A company want to set up a both and hand out samples of their new product, they will have to ask themselves if by handing out samples of their new product the sales of that product will make up for the cost of the samples and booth.
Population and Sample:
Population: All of the individuals in the group being studied.
- Example: The population in a federal election is all eligible voters.
Sample: Any group of individuals selected from the population.
- Example: A sample of the population in a federal election might be 100 individuals chosen from each province or territory.
Sampling Methods:
Convenience sample: A sample created by choosing individuals from the population who are easy to access.
- Example: If you wanted to find what pizza flavours were most popular among college students you could go to a local college and survey the students, you could then reliably say that your results are an accurate representation of most college students.
Random Sample: A sample created by choosing a specific number of individuals randomly from the whole population. Random means that each individual has an equal chance of being chosen. As a result it is likely to represent the whole population; data from random sampling can be used to make predictions about the population.
- Example: Adrian is thinking about moving permanently to a new town. However, he wants to get an idea of how the people in the town feel about the safety of the town, Adrian uses a phone book with all of the names of the people in the town as his population group. He then puts each name on a piece of paper and puts the papers into a bag. Adrian can blindly select a certain number of names from the bag, everybody has an equal chance of getting selected.
Stratified Sample: A type of random sampling created by dividing the whole population into distinct groups and then choosing the same fraction of members from each group.
- Example: High school students are separated into groups based on their grade, 20 people are then selected from each group and asked what their favourite subject is.
Systematic Sample: A type of random sampling created by choosing individuals at fixed intervals from an ordered list of the whole population.
- Example: Lucas is the manager of a movie theater, and wants to find out how the customers feel about the new renovations they’ve done at the theater, he can’t ask every customer that comes in so he decides to ask every 9th customer that walks in.
- His intervals would be: 9, 18, 27, 36, 45, 54, 63, 72, 81…
Voluntary Response Sample: A sample created by inviting the whole population to take part.
- Example: A Radio station asks for its viewers to take part in an online survey, based on the music they want to hear, the viewers have the choice of doing the survey or not.
How can choosing inappropriate sampling methods bias the data?
- If You pick the wrong sampling methods it may not appeal to the people you are trying to target , or if your wording isn’t right it may have the opposite effect of what you want.
- Example: If you went to Wendy’s and asked people walking out of the store if they liked Mcdonalds or Wendys more, this would be an inappropriate sampling method because they are more likely going to choose Wendy’s.
Theoretical and Experimental Probability:
Theoretical Probability: What we expect to happen during the probability test.
Experimental Probability: What the actual results are during the probability testing.
Example: If you flipped a coin 50 times the theoretical probability of getting heads or tails is 50 – 50, However when you actually do the experiment you could get 23 heads and 27 tails or vice versa, this would be experimental probability because these are the actual results of the probability testing.
Misleading Statistics:
This is misleading because the overall percentage doesn’t add up to 100% but instead adds up to an overall percentage of 120%. Another thing that is misleading about this chart is the way the data was put together, Fox News fused some of the data in their poll together. There were five options altogether, Very likely, somewhat likely, not very likely, not very likely at all, and a section for those who were unsure. They fused the data for “very likely” and “somewhat likely” together to get the 59%, they put “very likely” into its own group after they fused it with another, they fused together “not very likely” and “not very likely at all” to get 26%, and they removed all the data for those who were unsure.
This is very misleading because the chart makes it look like the players throwing speed in 2013 is two times less then what it was in 2012,but if you look closely at the actual numbers there is only a two mile per hour difference. They made the gap between the two graphs larger making it seem that there was a huge drop in the players speed.
Again this chart is misleading because it makes it look as if the amount of people on welfare is four times larger than the amount of people with a full time job, however, if you take a closer look at the numbers there is only a small difference between them. The data for this chart is also misleading because when Fox News was gathering the data if you lived in a household with someone who was (briefly) on some kind of welfare program, that counted against you and everyone in your household.
Created by: Keisha Nagorr, and Darren Phan
