This document provides an overview of basic statistical concepts. It defines key terms like population, sample, parameter, and statistic. It also describes different types of data like quantitative, qualitative, discrete, and continuous data. Additionally, it covers different levels of measurement like nominal, ordinal, interval and ratio scales. The document also discusses sampling methods such as random sampling and stratified sampling. Finally, it defines important concepts like sampling error, non-sampling error, reliability, and validity.

1. Statistics: Basic Concepts

2. Overview
• Survey objective:
– Collect data from a smaller part of a larger group
to learn something about the larger group.
• What is data ? How de we describe them?
– Observations (such as measurements, genders,
survey responses) collected.
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3. Statistics
• Statistics: Science which describes or make inferences
about the universe from sample information.
• Descriptive Statistics: Refers to numerical and graphic
procedures to summarize a collection of data in a
clear and understandable way.
• Inferential Statistics: Refers to procedures to draw
inferences about a population from a sample.
• In sum, Statistics refers to a set of methods to plan
experiments, obtain data, and then organize,
summarize, present, analyze, interpret, and draw
conclusions based on the data.
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4. Definitions
• Population: The set of all elements
(scores, people, measurements, and so on)
for study .
• Census: Collection of data from every
member of the population.
• Sample: a sub-collection of members drawn
from a population.
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5. Key Concepts
• Sample data must be collected in a scientific
manner, say, through a process of random
selection.
• If not, collected information will be useless
& statistical gymnastic would not salvage.
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6. Types of Data
• Parameter: A numerical measurement to
describe some characteristic of a population.
• Statistic: A numerical to describe some
characteristic of a sample.
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7. Definitions
• Quantitative data: Numbers representing
counts or measurements.
• Qualitative (categorical/attribute) data: Data
specified by some non-numeric
characteristics (for example, gender of
participants).
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8. Quantitative Data
Discrete: When the number of possible values
is finite or countable number of possible
values – 0,1,2,3,…
Example: Number of cars parked outside the
campus.
• Continuous: Infinite number of values
pertaining to some continuous scale without
gaps.
• Example: Milk yield of a cow.
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9. Levels of Measurement
• Nominal: Data on names, labels, or
categories that cannot be ordered.
• Example: Survey responses: Yes, No,
Undecided.
• Ordinal: Data that can be ordered but their
difference cannot be determined or are
meaningless.
• Example: Course grades A, B, C, D, or F
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10. Levels of Measurement
• Interval: Ordinal with the additional
property that difference between any two
values is meaningful but here is no natural
starting point (none of the quantity is
present).
• Example: Years: 1900, 1910,…
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11. Levels of Measurement
• Ratio: Modified interval level to include the
natural zero starting point- differences and
ratios are defined.
• Example: Prices of chocolates.
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12. Levels of Measurement
• Nominal - categories only
• Ordinal - categories with some order
• Interval - differences but no natural
starting point
• Ratio - differences and a natural starting
point
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13. Methods of sampling
• Random Sampling: Members of a population
selected in such a way that every member has
equal chance of getting selected.
• Simple Random Sample: Sample units selected
in such a way that every possible sample of the
same size n has the same chance of selection.
• Systematic Sampling: Select some staring point
and then select every k-th member in the
population
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14. Methods of sampling
• Convenience Sampling: Use results easy to
obtain.
• Stratified Sampling: Subdivide the population
into at least two different groups with similar
characteristics and draw a sample from each
group.
• Cluster Sampling: Divide the population into
clusters , randomly select clusters, choose all
members of the chosen clusters.
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15. Relevant Definitions
• Sampling error: Difference between a sample
estimate and the true population estimate –
error due to sample fluctuations.
• Non-sampling error: Errors due to mistakes in
collection, recording, or analysis (biased
sample, defective instrument, mistakes in
copying data).
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16. Relevant Definitions
• Reliability: An estimate is reliable when there
is consistency on repeated experiments.
• Validity: An estimate is valid when it has
measured what it is supposed to measure.
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