This document discusses statistical methods for collecting and analyzing data. It describes descriptive statistics such as collecting, presenting, and characterizing data using measures like the mean. Inferential statistics are used to make decisions about populations based on sample results through estimation and hypothesis testing. The document outlines different types of data and data sources, as well as sampling methods like probability and non-probability sampling. It provides details on specific probability sampling techniques including simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

1. Statistical Methods
•Descriptive Statistics
•Inferential Statistics
Collecting and describing data.
Making decisions based on sample data.

2. Descriptive Statistics
•Collect Data e.g. Survey
•Present Data e.g. Tables and Graphs
•Characterize Data e.g. Mean
n
x i

A Characteristic of a:
Population is a Parameter
Sample is a Statistic.

3. Inferential Statistics
•Estimation
•Hypothesis
Testing
Making decisions concerning a
population based on sample results.

4. Types of Data
Categorical
Discrete Continuous
Numerical
Data

5. Data Sources
Primary
Data Collection
Secondary
Data Compilation
Observation
Experimentation
Survey
Print or Electronic

6. Quota
Types of Sampling Methods
Samples
Non-Probability
Samples
Judgement Chunk
Probability Samples
Simple
Random
Systematic
Stratified
Cluster

7. Probability Samples
Probability Samples
Simple
Random
Systematic Stratified Cluster
Subjects of the sample are chosen based on known probabilities.

8. Simple Random Samples
•Every individual or item from the
target frame has an equal chance of
being selected.
•Selection may be with replacement or
without replacement.
• One may use table of random numbers
for obtaining samples.

9. Systematic Samples
• Decide on sample size: n
• Divide population of N individuals into groups of
k individuals: k = N/n
• Randomly select one individual from the 1st group.
• Select every k-th individual thereafter.
N = 64
n = 8
k = 8
First Group

10. Stratified Samples
• Population divided into 2 or more groups according
to some common characteristic.
• Simple random sample selected from each.
• The two or more samples are combined into one.

11. Cluster Samples
• Population divided into several “clusters”,
each representative of the population.
• Simple random sample selected from each.
• The samples are combined into one.
Population
divided into
4 clusters.

12. Types of Survey Errors
•Coverage Error
•Non Response Error
•Sampling Error
•Measurement Error
Excluded from
selection.
Follow up on
non
responses.
Chance differences
from sample to
sample.
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