The document outlines various data sources, emphasizing the distinction between primary and secondary data, and the importance of understanding biases in published information. It discusses different methodologies for data collection, including experiments, surveys, and sampling techniques like probability sampling, simple random sampling, and sampling with or without replacement. Additionally, it highlights the necessity of proper sampling methods to ensure reliable and valid statistical analysis.

1. Welcome To
Presentation
A.S.M Yeasser Arafat

2. Sources of Data

3. Published Sources
CONCEPT
Data available in print, electronic, Internet websites.
Primary data, Secondary data.
EXAMPLE
Many U.S. federal agencies are publish primary data in
internet website. Business news sections of daily
newspapers…
Why we need to use?
Possible bias of the publisher and whether the data contain
all the necessary and relevant variables

4. Experiments
CONCEPT
A study that examines the effect on a variable of varying the
value(s) of another variables. A typical experiment contains
both a treatment group and a control group.
EXAMPLE
Pharmaceutical companies use experiments to determine
whether a new drug is effective.
Why we need to use?
Proper experiments are either single-blind or double blind.

5. Surveys
Concept
A process that uses questionnaires or similar means
Example
likely voters, a website instant poll or “question of the
day.”
Why Needed?
open to anyone who wants to participate targeted, specific
group

6. Sampling Concepts
calculating statistics for a sample is the most common activity because
collecting population data is usually impractical

7. Frame
Concept
all items in the population from which the
sample
Example
Voter registration lists, municipal real
estate records
Why we need to use?
Frames influence the results of an
analysis, and using different frames

8. Sampling
Concept
The process by which members of a population are
selected for a sample
Examples
Choosing every fifth voter who leaves a polling place to
interview
Why we need to use?
Some sampling techniques, such as an “instant poll” found
on a web page, are naturally suspect as such techniques
do not depend on a well-defined frame.

9. Probability Sampling
Concept
A sampling process that considers the chance of
selection of each item.
Examples
the patients selected to fill out a patient-satisfaction
questionnaire
Why we need to use?
You should use probability sampling whenever possible,
because only this type of sampling enables you to apply
inferential statistical methods to the data you collect.

10. Simple Random Sampling
Concept
Every individual or item from a population has the same
chance of selection as every other individual or item.
Examples
Selecting a playing card from a shuffled deck or using a
statistical device, such as a table of random numbers.
Why we need to use?
•where not much information is available about the
population .
•an unbiased surveying technique

11. Sample Selection Methods

12. Sampling with Replacement
Concept
each selected item is returned to the frame
Examples
Selecting items from a fishbowl and returning each item
to it after the selection is made.
Why we need to use?
•the two sample values are independent
•what we get on the first one doesn't affect what we get
on the second

13. Sampling Without Replacement
Concept
each selected item is not returned to the frame from which
it was selected.
Examples
Selecting numbers in state lottery games, selecting cards
Why we need to use?
•the two sample values aren't independent
•this means that what we got on the for the first one
affects what we can get for the second one.