1. By Veronica and ricardo
Statistics
By Veronica and ricardo

2. Influence and collection of data
Language, ethics, cost, time and timing, privacy, and cultural sensitivity may influence the collection of data.

3. INFLUENCING FACTORS
Bias: Does the question show preference for the specific product?
Example: Asking the people who eat at McDonald's if they like mcDonald's is bias because if they didn't like McDonald's they wouldn't be there.
Use of Language: is the question presented in such a way that people don't understand what is being asked.
Example: using language that someone with common education can not understand will influence the survey because they might not know what the question is asking.

4. Influencing factors
Ethics: Does the question refer to inappropriate behaviour? Try to present the truth instead
Example: Asking someone if they think that 'the evil Donald trump who kills and deports people should be president' is way different than asking , 'should Donald trump be president?'. The wording in the question can make the person change their opinion. Companies pre-pick a point of view that will make them money.
Cost: Does the cost of the study outweigh the benefits?
Example: The government may not find the cost of asking all of Canada if they watch more than an hour of tv a day worth it. There are no benefits from that survey. They might want to ask who they think will be prime minister because there are real benefits from that question.

5. INFLUENCING FACTORS
Privacy: Do people have the right to refuse to answer? Are the responses kept confidential?
EXAMPLE: PEOPLE MAY NOT WANT TO PICK AN UNPOPULAR CHOICE IF THE SURVEY IS NOT CONFIDENTIAL.
Cultural Sensitivity: might the question offend people from different cultural groups?
EXAMPLE: ONE QUESTION THAT Would be offensive is if you ask Muslim people what their favourite brand of ham is.

6. INFLUENCING FACTORS
Time and timing: does the time the data were collected influence the results? Is the timing of the survey appropriate?
EXAMPLE: IF YOU ASK A TOWN IF THEY THOUGHT THAT THEY SHOULD GET EARTHQUAKE INSURANCE BEFORE AN EARTHQUAKE, THEIR ANSWERS WOULD DIFFER FROM IF YOU ASKED THEM AFTER AN EARTHQUAKE.

7. Population and sample
Population: all the individuals in the group being studied. For example, the population in a federal election is all eligible voters.
Sample: Any group of individuals selected from the population. For example, a sample of the population in a federal election might be 100 individuals chosen from each province or territory.

8. Sampling methods
Convenience sample: A sample created by choosing individuals from the population who are easy to access.
EXAMPLE: ASKING YOUR CLASS A SURVEY WOULD BE mORE CONVENIENT THAN ASKING YOUR WHOLE SCHOOL.
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. Data from a random sample can be used to make predictions about the population.
Example: Giving every student a different four digit code and randomly selecting a code to be surveyed.

9. Sampling methods
Stratified sample: A sample created by dividing the whole population into distinct groups, and then choosing the same fraction of members from each group.
Example: separate go the school into grades, and then asking 10 kids from every grade what their favourite ice cream is.
Systemic sample: a sample created by choosing fixed people from an ordered list of the whole population.
Example: asking every tenth person walks in the mall what their favourite store is.

10. Sampling Methods
Voluntary response sample: a sample created by inviting the whole population to participate
Example: Asking all citizens of Canada how many dogs they have by sending a letter that they have a choice of answering.
How different sampling methods might bias the data:
People who don't have any dogs have a lower chance of responding to the letter than someone with a dog.

11. Theoretical and experimental probability
Theoretical probability: The expected probability of an event occurring Example: if you roll a dice with numbers 1,2,3,4,5,6 on it, the theoretical probability is that you have a 1/6 chance of rolling a 5. Experimental probability: The probability of an event occurring based on experimental results Example: if you roll a dice 1000 times and and the number 2 shows up 180 times, then the experimental probability is 180/1000= 0.18

12. Misleading statistics in the media
This is misleading because although it is true that the hand sanitizer kills 99.99% of germs, it is only true in the lab. In real world settings, this is not true. People would most likely only use the product in real world settings, making the numbers irrelevant.

13. Misleading statistics in the media
This statistic is misleading because it makes Sweden look like the rape Capitol of Europe, but the data is classified differently. Sweden defines rape more broadly, so more things classified as rape would be reported.

14. Misleading statistics in the media
This graph is misleading because the Y axis starts at 50, making the difference look much greater. If the Y axis started at 0, everything would look pretty equal.