These introductory statistics slides will give you a basic understanding of statistics, types of statistics, variable and its types, the levels of measurements, data collection techniques, and types of sampling.

1. Statistics
Saed Jama
siciid522@Hotmail.com
17 March, 2017

2. Introduction to Statistics
The study of statistics is become more popular than ever in the last decades. Statistics is used in al
most every field. Almost all newspapers and magazines these days contain graphs and stories on
statistical studies.

3. What is statistics?
Every day we make decisions. These decisions are made under conditions of uncertainty. Many
times, the problems we face in the real world have no precise solution. Statistical methods help us
make scientific and intelligent decisions in such situations.
Statistics is the study of how to collect, organize, analyze, and interpret numerical information from
data to make decision.

4. Branches of Statistics
Applied statistics can be divided into two main parts; descriptive statistics and inferential statistics.
Descriptive statistics: descriptive statistics consists of methods for organizing, displaying, and
describing data using tables, graphs and summary measurement.
inferential statistics: inferential statistics consists methods that use sample results to make decision of
prediction about population.
As the other disciplines have, statistics has its own terminology. However, before we go forward we
will look terminologies used in statistics 101 or introduction to statistics.

5. Terminologies in statistics
Population vs Sample
Population: Population is the group you are interested in studying. It consists of all elements, items
or objects whose characteristics are been studying.
Sample: Sample is a portion of population under the study
Population Sample

6. Example:
You want to find the monthly income of all students in your university and your university has
10,000 students, and you selected 500 students to ask their monthly income.
Your population is the group you are interested which in this example is the 10,000 students and
sample is a portion you selected from the population which is 300 students.
10,000 students
300
students

7. In statistics data can be from either census or sample survey, therefore we look here brief description
about both of these terms.
Census survey: A survey that includes every member of the population is called a census.
Sample Survey: The technique of collecting information from a portion of the population is called a
sample survey.
Representative Sample: A sample that represents the characteristics of the population as closely as
possible is called a representative sample.
Census vs Sample Survey

8. Parameter: A numerical observation describing some characteristic of a population is known as
Parameter.
Statistic: A numerical observation describing some characteristic of a sample is known as statistic.
Example: Your are interesting in knowing the average monthly income of all UOH students. You
observe a random sample of 500 UOH students and find that their average monthly income is $150.
in this example.
Population: The monthly income of all UOH students
Sample: The monthly income of the 500 sampled student
Parameter: The average monthly income of all UOH students
Statistics: The average monthly income of 500 samples students = $150/month.
Statistic vs Parameter

9. Variable & Types of Variables
A Variable: variable is any characteristic or quantity that can take on many values.
For example:- “Age” is a variables and can take many values like 10, 15, 20 etc.
“Gender” is also a variable and can take two distinct values like Male or Female.
The two types of variables; Quantitative and Qualitative/categorical variables.
Quantitative variable: A variable that can be measured numerically is called a quantitative variable.
Age, Incomes, heights, gross sales, and number of accidents are examples of quantitative variables.

10. Quantitative variable can be sub-divided into two parts:
Discrete variable: A variable whose values are countable is called a discrete variable. In other words,
a discrete variable can assume only certain values with no intermediate values.
Number of people visiting a hospital on any day, number of camels owned by a person, number of
students in a class is example of discrete variable.
Continuous variable: A variable that can assume any numerical value over a certain interval or
intervals is called a continuous variable. Height, Weight are examples of continuous variable.
Types of Variables

11. Qualitative Variable: A variable that cannot assume a numerical value but can be classified into two
or more nonnumeric categories is called a qualitative or categorical variable.
Gender (Male & Female), hair colour (Black, Brown, Red, Yellow), the status of an undergraduate
college student (freshman, sophomore, junior, or senior) are examples of qualitative variables.
Qualitative/Categorical Variable

12. Variable
Quantitative
Discrete
e.g. number of
house, cars,
mobiles etc.
Continuous
e.g. length,
height, weight,
time etc.
Qualitative or
Categorical
Figure: Types of Variables

13. Measurement is the process of applying numbers to objects according to set of rules. However,
according to introductory statistics text books there are four scales of measurements.
Nominal: Assign numbers to objects where different numbers indicate different objects. The numbers
have no real meaning other than differentiating between objects.
Example: Gender: 1 = male, 2 = female
eye color: 1 = black, 2 = brown, 3 = others
Marital status: 1 = Single, 2 = Married, 3 = Divorced, 4 = Separated etc.
Scales of Measurements

14. Ordinal: assign numbers to objects but the numbers have meaningful order.
Example: Car race: First, Second, Third
Service satisfaction: 1 = very unsatisfied, 2 = somewhat satisfied, 3 =
neutral, 4 = somewhat satisfied, 5 = very satisfied.
All these examples the difference between number have meaningful.
Interval: This scale of measurement has the properties of the nominal and ordinal scales, but here
there are equal intervals between objects. In interval scales there is no true zero.
Example: The difference between 40 and 50 degrees is the same as between 70 and 80 degrees. It is
wrong to say that 80 degree is twice as hot as 40 degree.
Scales of Measurements

15. Ratio: Ratio scale of measurement is similar to the interval scale, but has an absolute zero and
multiples are meaningful.
Example: A temperature of 200 Kelvin is twice as high as a temperature of 100 Kelvin.
Scales of Measurements

16. In the previous slides we have defined statistics as methods of interpreting numerical information
from data to make decisions. Therefore, it is important that you familiar with various kinds of data
collection techniques.
Variety of techniques and procedures available for collecting data. Some of the most common data
collection methods are:
1. Interview
2. Observations
3. Questionnaires
4. Focus group discussion and many other methods.
Each method has advantages and disadvantages
Data Collection Methods

17. Interviews is a method of data collection, usually one-on-one between an interviewer and an
individual or as a groups. Interviews can be conducted in person or over the phone
Answers to the questions posed during an interview can be recorded by writing them down or by tape
recording the responses, or by a combination of both.
Interviews can be done formally (structured interview) or informally (semi-structured interview).
Questions should be clear, focused, and encourage open-ended responses.
1. Interviews

18. Advantages:
o Selection of suitable candidate
o Depth of the responses can be assured
o Clarification is possible
o High proportion of responses is found etc.
Disadvantages:
o Lack of attention from both the interviewer and the interviewee.
o Time consuming and expensive compared to other data collection methods
o May seem disturbing to the respondent etc.
Advantages and Disadvantages

19. Observation is another data collection technique under which data from the field is collected with the
help of observation by the observer.
Advantages:
o Bias can be eliminated (no bias)
o Information is update
o Less cost compared to the interview method
Disadvantages:
o Time consuming
o Limited information
o Respondents opinion can not be recorded
2. Observations

20. Questionnaire is a general term of data collection in which each person is asked to reply to the same
set of questions in a predetermined order. (deVaus, 2002). Attribute variables contain data about the
respondents characteristics. The questionnaire can be open-ended or closed ended type.
Advantages:
o The responses are gathered in a standardised way, so questionnaires are more objective, certainly
more so than interviews.
o Generally it is relatively quick to collect information using a questionnaire
o Potentially information can be collected from a large portion of a group.
Disadvantages:
o Participants may forget important issues.
o Open-ended questions can generate large amounts of data that can take a long time to process and
analyse.
o Respondents may not be willing to answer the questions.
3. Questionnaire

21. A small group of people gathered to discuss their perceptions, opinions, beliefs, and attitudes about a
certain topic. In this form of data collection interaction and group dynamic are essential.
In focus group discussion investigators interview people with common qualities or experience for
eliciting ideas, thoughts and perceptions about particular subject areas or certain issues associated
with an area of interest.
There are two main reasons to have focus groups
1. Get the concept and idea right
2. Pre-test you interview or questions before you collect data.
4. Focus Group

22. Advantages:
o Fast and lower cost method of acquiring valuable data.
o Participants are given a chance to reflect or react to the viewpoint of others with which they may
disagree or of which they’re unaware.
o All individuals along with the researcher have a chance to ask questions.
o The researcher can clarify clashes among participants and ask about these diverse opinions.
Disadvantages:
o Data analysis could be time consuming and challenging task.
o they can be hard to control and manage.
o The small sample size means the groups might not be a good representation of the larger
population.
Advantages and Disadvantages of Focus Group

23. We have defined the previous slides, that a sample is a method of gathering information from portion
of population.
However, the following slides we will explain different methods of obtaining a sample.
There are two main different types of Sampling
1. Probability Sampling
2. Non-probability Sampling
Each can be sub-divided into many different parts, but first, lets examine probability sampling and
types of probability sampling.
Sampling Techniques

24. Probability sampling is technique in which each member of population has an equal chance of being
selected. The main purpose of sampling is to create a sample that is representative of the population it
is being drawn from hence it is very difficult to survey the whole population.
Probability sampling can be sub-divided into many different types;
1. Simple Random Sampling
In Simple Random sampling, each member of the population (N) has the same probability (chance) of
being selected for your sample (n). Sometimes this is called a Lottery method.
Arguably, this is the best sampling method but very difficult.
Probability Sampling

25. 2. Stratified Sampling
In Stratified sampling, the population (N) is split into homogeneous groups called strata and then use
Simple Random sample within each stratum to form sample (n).
Example;
this would splitting a population of students (N) in a university into men and women, and then
selecting sample (n) from each of the two groups (strata).
This allows us to collect same amount of information as simple random sampling, but use less people
because people are already divided into male and female.
Probability Sampling

26. 3. Systematic Sampling
Systematic Sampling technique involves selecting every Kth item in a population (N) after a
randomly selected starting point between 1 and k. the k value is determined as the ratio of the
population size over desired sample size(n).
Example:
If you select every 5the person to walk out of a supermarket to your sample after randomly selected
the person you start for the sampling, you are performing Systematic Sampling.
Probability Sampling

27. 4. Cluster Sampling
Cluster Sampling is a method by which the population is divided into separate groups, called
clusters, then a simple random sample of clusters is selected from the population.
Splitting the population in this way into parts or clusters that each represent the population can make
sampling more practical. We select one or a few clusters at random and perform a census within each
of them. This sampling design is called cluster sampling.
Probability Sampling

28. Non-probability sampling is a sampling technique where the sample are gathered in a process that
does not give all the individuals in the population equal chances of being selected.
1. Convenience Sampling
Convenience sampling is a non-probability sampling method that selects the item from the population
based on accessibility and ease of selection.
This sampling technique, the subjects are chosen simply because they are easy to recruit. It is easy,
cheap and least time consuming data collection technique but has many disadvantages.
Non-probability Sampling

29. 2. Quota Sampling
Quota Sampling is a Non-probability sampling focuses on sampling techniques that are based on the
judgement of the researcher.
The aim is to end up with a sample where the strata (groups) being studied (e.g., males vs. females
students) are proportional to the population being studied.
Example:
The number of students from each group that we would include in the sample would be based on the
proportion of male and female students amongst the 10,000 university students.
Non-probability Sampling

30. 3. Snowball Sampling
Snowball sampling is appropriate to use when the population you are interested in hard-to-reach.
These include populations such as drug addicts, homeless people, individuals with AIDS/HIV,, and so
forth.
4. Judgment Sampling
Judgment sampling, reflects a group of sampling techniques that rely on the judgement of the
researcher when it comes to selecting the items (e.g., people, organisations, events of data) that are
to be studied.
Non-probability Sampling

31. Sampling Techniques
Sampling
Technique
Probability
Simple Systematic Stratified Cluster
Non-
probability
Convenience Quota Snowball Judgment