Penn State

1. DATA ANALYSIS
17 September 2015

2. TERMS, DEFINITIONS,AND APPROACH

3.  Population versus sample.
 Parameter versus statistic.
 Inference of population parameters from
sample statistics.

4.  Population
• Any complete group with at least one characteristic in
common.
• Not just people, but any entity.
• Might consist of, but not limited to, people, animals,
businesses, buildings, motor vehicles, farms, objects, or
events.
 Sample
• A group of units selected from a larger group (the
population).
• Generally selected for study because the population is too
large to study in its entirety.
• Good samples represent the population.

5. List 10 examples of
population/sample
pairs.

6.  Parameter
• Information about a population.
• Characteristic of a population.
• A population value.
• The “truth.”
 Statistic
• Information about a sample.
• An estimate of a population value.

7. List 10 examples of
parameters and
associated statistics

8.  Data usually are available from a sample, not a
population.
 That is, sample statistics are available, not population
parameters.
 We wish to infer (or estimate) parameters from
statistics.
 Because data are available from a sample, not the
population, error occurs when inferring (or estimating)
population parameters from sample statistics.
 Data analysis techniques help us make decisions
under error and uncertainty.

9. THEORY, PROPOSITIONS, LOGIC

10.  Are composed of propositions that explain the
empirical, observable world. A proposition is an
“if–then” statement
 Are networks showing relationship and causality
among propositions.
 Must have“empirical import.”

11.  The foundation of theory-building.
 Statements of testable scientific
propositions.
 The focus for empirical work.

12.  Examine propositions in theory that require
verification.
 Are specific.
 Are testable.

13. The term "nomological" is derived from Greek
and means "lawful.”
A nomological network is a"lawful network,” a
network of propositions that describe how
things work.

17.  Chapter 1 treats
concepts in the
philosophy of science

18. Describe 1 example
of theory and 1
example of a
pseudo-theory

19.  Hypotheses are“tested”
 Hypotheses are never“proved”
 Hypotheses only are“rejected”
 Theories are built and verified by testing hypotheses

20.  Research is designed to evaluate whether on–
the–job training reduces cycle time in product
manufacturing.
 Two groups of subjects:
• One group receives on-the-job training.
• The other group receives classroom training.
 Dependent variable is cycle time;
independent variable is group membership.

21.  Greek letters used to designate parameters.
 Letters of English alphabet used to signify
statistics.

22.  Null hypothesis is H0: m1 - m2 = 0 stated
about parameters.
• Equivalent to m1 = m2
• Estimated by testing whether mean1 = mean2.
• E.g., estimated by testing if mean cycle timeon-the-
job training = mean cycle timeclassroom training.
 Alternate hypothesis is H1: m1 - m2 not equal 0.
• Equivalent to m1 ≠ m2.

23. Formulate 1
statistical null
hypothesis & and its
alternative

24. Decision
Fail to
reject Ho
Reject Ho

25. Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

26. Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho
Where are errors?

27. Error
Error
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

28. Error
Error
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho
What do the
errors cost?

29. Type 1
error
Error
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

30. Type 1
error
Type 2
error
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

31. MinimizeType 1
error by selecting
low error rate
Type 2
error
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

32. MinimizeType 1
error by selecting
low error rate
MinimizeType 2
error by
increasing
sample size
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

33. TRADITIONALLY,
probability ofType 1
error set at .05
MinimizeType 2
error by
increasing
sample size
Truth
Ho true Ho false
Decision
Fail to
reject Ho
Reject Ho

34. In a decision-by-
truth table, describe
possible outcomes
of a statistical null
hypothesis test

35. DATA ANALYSIS
17 September 2015