The document discusses key concepts in statistics including populations, samples, parameters, statistics, experimental design, and sampling techniques. It defines a population as all possible outcomes of interest and a sample as a subset of a population. Experimental design aims to control for confounding variables through randomization and replication. There are different sampling techniques for selecting samples such as simple random sampling, stratified sampling, and cluster sampling. Descriptive and inferential statistics are used to analyze and draw conclusions from data.

2. Statistics – The science of collecting, organizing, analyzing
and interpreting data in order to make decisions
Data – information coming from observations, counts,
measurements, or responses
Where have you seen statistics being used before?

3. DATA SETS: POPULATIONS VS. SAMPLES
A population is the collection of all outcomes, responses,
measurements, or counts that are of interest
A sample is a subset of a population

4. In a recent survey, 1708 adults in the US were asked if they
think global warming is a problem that requires immediate
government action. 939 of the adults said yes.
Identify the population and the sample.

5. The US Department of Energy conducts weekly surveys of
approximately 800 gasoline stations to determine the average
price per gallon of regular gasoline. On Feb. 12, 2007, the
average price was $2.24 per gallon.
Identify the population and the sample.

6. Parameter – a numerical description of a POPULATION
characteristic
Statistic – a numerical description of a SAMPLE characteristic
**P’s stay together, and S’s stay together
**Population = parameter
**Sample = statistic

7. DISTINGUISH BETWEEN A PARAMETER AND STATISTIC
1. A recent survey of a sample of MBAs reported that the average
salary for an MBA is more than $82,000.
2. Starting salaries for the 667 MBA graduates from the University
of Chicago Graduate School of Business increased 8.5% from
the previous year.
3. In a random check of a sample of retail stores, the Food and
Drug Administration found that 34% of the stores were not
storing fish at the proper temperature.
4. In 2006, major league baseball teams spent a total of
$2,326,706,685 on players’ salaries.

8. BRANCHES OF STATISTICS
Descriptive Statistics – the branch of
statistics that involves the organization,
summarization, and display of data.
Inferential Statistics – the branch of
statistics that involves using a sample to draw
conclusions about a population.

9. SECTION 1.1 ASSIGNMENT
Pg. 8 - 11 #1 - #36 ALL

11. DATA CLASSIFICATION
Data can be just about ANYTHING pertinent to the question at hand:
Data about Students at BJSHS:

13. TYPES OF DATA
Qualitative Data – consists of attributes, labels, or
nonnumerical entries (movie ratings, favorite color, teams,
etc…)
Quantitative Data – consists of numerical measurements or
counts (amounts, times, etc…)
NOTE: NUMBERS DO NOT MEAN QUANTITATIVE

14. LEVELS OF MEASUREMENT
1.Nominal – qualitative only
2.Ordinal – qualitative or quantitative
3.Interval – quantitative only
4.Ratio – quantitative only

16. LEVELS OF MEASUREMENT
1. Nominal – categorized by names, labels or qualities
Yes/No Questions
Jersey Numbers
Names
Hair Color

17. 2. Ordinal – able to be ranked or ordered, difference mean
nothing particular
S/M/L/XL shirts
1st, 2nd, 3rd,…
Movie Ratings

18. 3. Interval – when 0 does NOT mean “nothing”; can’t find
ratios
Temperature
Years (NOT TIME BETWEEN THINGS)
4. Ratio – when 0 means “none” or “nothing”; true count, ratio
between two data points can be formed
Population
# of pages in a book
Length
Price/Money

19. SECTION 1.2 ASSIGNMENT
Case Study on Page 17 (SUBMIT) [Groups of 3 or less]
INDIVIDUAL:
Pg. 15 – 16 #1 - #24 ALL
(Level of Measurement means: nominal, ordinal, interval or ratio)

21. DESIGNING A STATISTICAL STUDY
1.Identify the variables
2.Develop a plan for collecting data
3.Collect the data
4.Describe the data (using DESCRIPTIVE statistics)
5.Interpret the data (using INFERENTIAL statistics)
6.Identify any possible errors.

22. DATA COLLECTION
1.Do an Observational Study
2.Perform an Experiment
3.Use a Simulation
4.Use a Survey

23. DATA COLLECTION
1.Observational Study
- Researcher observes and measure
characteristics of interest, but does NOT
change existing conditions.

24. DATA COLLECTION
2. Perform an Experiment
- a TREATMENT is applied to part of a population and
responses are observed
- Control Group – part of population where NO treatment is
applied
- Subjects are given a PLACEBO – harmless, unmedicated
treatment that is made to look like the real treatment
- Effects of treatment can be compared to control group
- Subjects of a study also knows as EXPERIMENTAL UNITS

25. DATA COLLECTION
INSIGHT
IN AN OBSERVATION STUDY, A RESEARCHER
DOES NOT INFLUENCE THE RESPONSES, IN
AN EXPERIMENT, A RESEARCHER
DELIBERATELY APPLIES A TREATMENT
BEFORE OBSERVING THE RESPONSES.

26. DATA COLLECTION
3. Use a Simulation
- Use of a mathematical or physical
model to reproduce the conditions
of a situation or process
- Allows you to study situations that
are impractical, or dangerous
- Saves companies time and money

27. DATA COLLECTION
4. Use a Survey
- An investigation of one or more characteristics
of a population
- Customer Service Surveys
- QUESTIONS MUST BE WORDED SO THEY DO
NOT LEAD TO BIASED RESULTS

28. Which method of data collection would you use
to collect data for each study?
1.A study of the effect of exercise on relieving
depression?
2.A study of the success of graduates of a large
university finding a job within on e year of
graduation.

29. EXPERIMENTAL DESIGN
3 KEY ELEMENTS OF A WELL-
DESIGNED EXPERIMENT
1.CONTROL
2.RANDOMIZATION
3.REPLICATION

30. EXPERIMENTAL DESIGN: CONTROL
Confounding variable – occurs when an experimenter
cannot tell the difference between the effects of
different factors on a variable
Example:
- Coffee Shop owner redecorates to attract more
costumers
- At the same time, a shopping mall nearby has a grand
opening
- VARIABLES ARE CONFOUNDED

31. EXPERIMENTAL DESIGN: CONTROL
PLACEBO EFFECT – when a subject reacts favorably to a
placebo when in fact, he or she has been given no
medicated treatment at all
To avoid this, we use BLINDING

32. EXPERIMENTAL DESIGN: CONTROL
BLINDING – WHEN THE SUBJECT DOES NOT
KNOW WHETHER HE OR SHE IS RECEIVING A
TREATMENT OR A PLACEBO
DOUBLE BLINDING – NEITHER THE SUBJECT
NOR THE THE EXPERIMENTER KNOWS IF THE
SUBJECT IS RECEIVING A TREATMENT OR
PLACEBO (PREFERRED)

33. EXPERIMENTAL DESIGN: RANDOMIZATION
Randomization – process of randomly
assigning subjects to different treatment
groups
1.Completely Randomized Design
2.Randomized Block Design
3.Matched Pairs Design

34. EXPERIMENTAL DESIGN: RANDOMIZATION
2. Randomized Block Design
- Divide subjects with similar
characteristics into blocks, and
randomly assign subjects to
treatments within each block
All
Subjects
30 – 39
year olds
Control
Treatment
40 – 49
year olds
Control
Treatment

35. EXPERIMENTAL DESIGN: RANDOMIZATION
3. Matched-Pairs Design
- Subjects are paired according to a similarity
- Subjects may be paired based on age,
residency, etc.
- One receives one treatment, and the other
receives another treatment

36. EXPERIMENTAL DESIGN: REPLICATION
Replication – repetition of an experiment using a
large group of subjects
- More subjects, more value added to the result
of your experiment
- We’re always looking for a large sample size

37. SAMPLING TECHNIQUES
1. Census – count or measure of ENTIRE population
2. Sampling – count or measure of PART of a population
- Random Sample
- Simple Random Sample
- Stratified Sample
- Cluster Sample
- Systematic Sample
- Sampling Error – difference between the results of a sample
and those of the population

38. SAMPLING TECHNIQUES
Sampling Error – difference between the results of a
sample and those of the population
Biased Sample – one that is NOT representative of the
population from which it is drawn.
Example: A sample of 18 – 22 year old college students
would NOT be representative of the entire 18 – 22
year old population in the country.

39. SAMPLING TECHNIQUES
Random Sample – every member of the population has
an equal chance of being selected
Simple Random Sample – every possible sample of the
same size has the same chance of being selected
USE OF RANDOM NUMBER GENERATORS!

40. SAMPLING TECHNIQUES
WHEN IT IS IMPORTANT FOR THE SAMPLE TO HAVE MEMBER FROM
EACH SEGMENT OF THE POPULATION
Stratified Sample – members of population are divided into two or
more subsets (strata), then sample is randomly selected from
each strata
**Ensures that each segment of the population is represented

41. SAMPLING TECHNIQUES
WHEN THE POPULATION FALLS INTO NATURALLY OCCURRING
SUBGROUPS
CLUSTER SAMPLE – Divide the population into groups (clusters),
and select ALL of the members in one or more (but NOT ALL) of
the clusters.
**Must be important that all clusters have similar characteristics

43. SAMPLING TECHNIQUES: INSIGHT
For STRATIFIED SAMPLING, each of the strata contains
members with a certain characteristic.
For CLUSTERS, each consist of geographic groupings,
and should consist of members with ALL
characteristics.
- Stratified – Some of members of each group are used
- Cluster – All of members of one or more groups are
used

44. SAMPLING TECHNIQUES
- Systematic Sample – a sample in which each
member of the population is assigned a number,
those members are then ordered and then
sample members are selected at regular intervals
starting with the starting number.
# # # # # # # # #

45. SAMPLING TECHNIQUES
Convenience Sample – sample consists
only of available members of population
(not recommended)

46. ASSIGNMENT
Pg. 25 #1 - #14, #17 - #26 (identify sampling technique)
Pg. 27 #29- #30

47. HOMEWORK SELECTED ANSWERS
Section 1.1
5. False
6. True
7. True
8. False
9. False
10. True
11. Pop
12. Sam
13. Sam
14. Pop
15. Sam
16. Pop
21. Pop: all adults in US
Sam: 1000 surveyed
22. Pop: all infants in Italy
Sam: 33043 infants in
study
23. Pop: all households in
US
Sam: 1906 households
surveyed
24. Pop: all computer users
Sam: 496 students
surveyed
29. Statistic
30.Statistic
31.Parameter
32. Parameter
33. Statistic
34. Parameter
35. Statistic
36. Parameter
Section 1.2
1. N and O
2. O, I and R
3. False
4. False
5. False
6. False
7. Qualitative
8. Quantitative
9. Quantitative
10. Qualitative
11. Qualitative
12. Quantitative
13. Qualitative, O
14. Qualitative, N
15. Qualitative
16. Quantitative, R
17. Qualitative, O
18. Quantitative, R
19. O
20. R
21. N
22. R
23. I, N, R, O
24. I,N,I,R
Section 1.3
5. True
6. False
7. False
8. False
9. Fasle
10. True
11. P an E
12. Survey
13. Simulation
14. Census
17. SRS
18. Stratified
19. Convenience
20. Cluster
21. SRS
22. Systematic
23. Stratified
24. Convenience
25. Systematic
26. SRS
29. Census
30. Survey