1. Why Study Statistics?
Statistics is the study of collecting, displaying, analyzing, and interpreting information. Information that was collected in a careful and systematic way is called data. We can use that data to better understand the world, and make better decisions about how we live.

2. A population is a collection of all outcomes, responses, measurements, or counts that are of interest.
A sample is a subset of a population.

3. Is it an example of a Population or a Sample?
The age of all High School principals.
A survey of 300 students out of 1000 Liberty Students.
The color of hair of every 4th girl that leaves a salon.
The salaries of Liberty teachers.

4. Identify the target Population and Sample
A survey of 1,000 U.S. adults found that 59% think buying a home is the best investment a family can make. (source: Rasmussen Reports).
A study of 33,043 infants in Italy was conducted to find a link between a heart rhythm abnormality and sudden infant death syndrome. (source: New England Journal of Medicine).

5. Biased Sampling Census vs Sample
If you are not careful about how you choose your sample, you can introduce bias into your data. Bias is a systematic favoring of one outcome.
Example:
You ask your classmates in Advanced Algebra what theme should be used for prom.

6. Biased Sampling
To avoid bias, you want to have a sample that is representative of the whole population. A representative sample is a small group from the population that is like the population in some important way.
Example:
You take a random sample of students at Liberty to decide what theme should be used for prom.

7. Sampling Techniques
A random sample is one in which every member of the population has an equal chance of being selected.
Ex: drawing numbers from a hat
A convenience sample is one that consists of only easily available subjects.
Ex: asking your friends what TV shows they like

8. Sampling Techniques Systematic sample
Each member of the population is assigned a number
Members of the population are ordered
Samples are taken from the population at regular intervals (ie: every 3rd, or 25th etc. individual)

9. Sampling Techniques Systematic Sample Example:
Every 3rd individual is in this sample

10. Sampling Techniques Stratified Sample
Has members of each segment of the population
Members are divided into subsets called strata, sharing similar characteristics, such as age, gender, ethnicity
A sample is randomly selected from each strata
Ensures that each segment of the population is represented

11. Sampling Techniques
Stratified Sample Example

12. Sampling Techniques Stratified Sample Example:
To decide the theme for this year’s prom, you divide Liberty’s student population into males/females and then randomly select students from both groups (strata)

13. Sampling Techniques Self-selected (Volunteer) Sample:
Subjects from the chosen population volunteer to be in the sample.
Example:
You post a question on social media to vote on a list of formal themes.

14. Which sampling technique is used?
Is the sample biased (non-representative)?
The names of 40 contestants are written on 40 cards. The cards are placed in a bag, and three names are drawn from the bag to announce winners
Random sample: non-biased
To avoid working late, the quality control manager inspects the last 50 items produced that day.
Convenience sample: biased
To ensure customer satisfaction, every 20th phone call received by customer service will be monitored.
Systematic sample: non-biased

15. Stratified sample: non-biased Volunteer sample: biased
Which sampling technique is used?
Is the sample biased (non-representative)?
Using student ID numbers, you randomly select 25 students from each class; Freshmen, Sophomores, Juniors, and Seniors.
Stratified sample: non-biased
To find out whether you enjoyed a book you just read, Amazon.com asks you to complete a survey and rate the book when you've finished.
Volunteer sample: biased