Explaing basic information about introduction of statistics, its uses and types of statistics.

1. STATISTICS
PRESENTATION
PRESENTED BY:
AREEBA SULTAN, AMMARA MANZOOR, GHAYUR KHALID WERYAH,
ALI HUSSAIN, AYESHA SHAFQAT.

2. DEFINATION OF STATISTICS
• The practice or science of collecting and
analysing numerical data in large quantities.
• A branch of mathematics dealing with the
collection, analysis, interpretation, and
presentation of masses of numerical data.
• A collection of quantitative data.

3. EXAMPLE OF STATISTICS
• In a class, the collection of marks obtained by 50 students is the
description of data. Now when we take out the mean of the data, the
result is the average of marks of 50 students. If the average mark
obtained by 50 students is 88 out of 100, then we can reach to a
conclusion or give a judgment on the basis of the result.

4. STATISTICAL DATA LAYOUT WITH
CHART
4.3
2.5
3.5
4.5
2.4
4.4
1.8
2.8
2 2
3
5
0
1
2
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5
6
BUDGET 1ST YEAR BUDGET 2ND YEAR BUDGET 3RD YEAR BUDGET 4TH YEAR
INFLATION PROFITS CURRENT ACCOUNT DEFICITS

5. TYPES OF STATISTICS
Statistics have majorly categorised into two types:
1. Descriptive Statistics.
2. Inferential Statistics.

6. DESCRIPTIVE STATISTICS
• In this type of statistics, the data is summarised through the given
observations. The summarisation is one from a sample of population
using parameters such as the mean or standard deviation.
• Descriptive statistics is a way to organise, represent and describe a
collection of data using tables, graphs, and summary measures.
• For Example, the collection of people in a city using the internet or using
Television.

7. DESCRIPTIVE STATISTICS
Descriptive statistics are also categorised into four different
categories:
• Measure of frequency.
• Measure of dispersion.
• Measure of central tendency.
• Measure of position.
The frequency measurement displays the number of times a particular data
occurs. Range, Variance, Standard Deviation are measures of dispersion. It
identifies the spread of data.

8. INFERENTIAL STATISTICS
• This type of statistics is used to interpret the meaning of Descriptive
statistics. That means once the data has been collected, analysed and
summarised then we use these stats to describe the meaning of the
collected data.
• It is used to draw conclusions from the data that depends on random
variations such as observational errors, sampling variation, etc.
• Inferential Statistics is a method that allows us to use information
collected from a sample to make decisions, predictions or inferences from
a population.
• For Example, deriving estimates from hypothetical research.

9. IMPORTANCE OF STATISTICS
 Statistics executes the work simply and gives a transparent picture of the
work we do regularly.
 The statistical methods help us to examine different areas such as
medicine, business, economics, social science and others.
 Statistics equips us with different kinds of organised data with the help
of graphs, tables, diagrams and charts.

10. IMPORTANCE OF STATISTICS
 Statistics helps to understand the variability of the data pattern in a
quantitative way.
 Statistics makes us understand the bulk of data in a simple way.
 Statistics is the way to collecting accurate quantitative data.

11. USE OF STATISTICS IN REAL LIFE
• Statistics play an important role in real life,
especially in large industries, where data is
computed in bulk. It helps to collect, analyse
and interpret the data. Also, with the help of
statistical graphs, charts and tables we can
easily present the data.

12. APPLICATIONS OF STATISTICS IN
DIFFERENT FIELDS
• Statistics plays a vital role in every field of human activity. Statistics
helps in determining the existing position of per capita income,
unemployment, population growth rates, housing, schooling medical
facilities, etc., in a country.
• statistics holds a central position in almost every field, including industry,
commerce, trade, physics, chemistry, economics, mathematics, biology,
botany, psychology, astronomy, etc., so the application of statistics is very
wide.
• “Now we shall discuss some important fields in which statistics is
commonly applied in the next slides.”

13. APPLICATIONS OF STATISTICS
BUSINESS
• Statistics plays an important role in business. A successful businessman
must be very quick and accurate in decision making. He knows what his
customers want; he should therefore know what to produce and sell and
in what quantities.
• Statistics helps businessmen to plan production according to the taste of
the customers, and the quality of the products can also be checked more
efficiently by using statistical methods. Thus, it can be seen that all
business activities are based on statistical information.

14. APPLICATIONS OF STATISTICS
ECNOMICS
• Economics largely depends upon statistics. National income accounts are
multipurpose indicators for economists and administrators, and
statistical methods are used to prepare these accounts.
• In economics research, statistical methods are used to collect and analyse
the data and test hypotheses. The relationship between supply and
demand is studied by statistical methods; imports and exports, inflation
rates, and per capita income are problems which require a good
knowledge of statistics.

15. APPLICATIONS OF STATISTICS
SOCIAL SCIENCES
• Statistics plays a vital role in almost all the social sciences.
• Statistical methods are commonly used for analysing experiments results,
and testing their significance in biology, physics, chemistry, mathematics,
meteorology, research, chambers of commerce, sociology, business, public
administration, communications and information technology, etc.

16. ADVANTAGES AND DISADVANTAGES OF
STATISTICS
ADVANTAGES
• The bulk data can be presented
in a precise and definite form.
• The comparison and conclusions
of data becomes easy.
• Forecasting the trends becomes
easy with statistics.
DISADVANTAGES
• The statistical data can lead to
misuse.
• There are chances of errors
becomes easy when the
statistical methods are not done
by the experts.
• For the comparison of the data,
the data should be homogeneous
and uniform.

17. SAMPLING
• Sampling is a statistical procedure that is concerned with the selection of
the individual observation; it helps us to make statistical inferences about
the population.
• Quality assurance, and survey methodology, sampling is the selection of a
subset of individuals from within a statistical population to estimate
characteristics of the whole population. Statisticians attempt to collect
samples that are representative of the population in question.

18. EXAMPLE OF SAMPLING
• if a drug manufacturer would like to research the adverse side effects of a
drug on the country’s population, it is almost impossible to conduct a
research study that involves everyone.
• In this case, the researcher decides a sample of people from each
demographic and then researches them, giving him/her indicative
feedback on the drug’s behaviour.

19. CHARACTERISTICS OF SAMPLING
•In sampling, we assume that samples are drawn from the population
and sample means and population means are equal. A population can be
defined as a whole that includes all items and characteristics of the
research taken into study. However, gathering all this information is time
consuming and costly. We therefore make inferences about the population
with the help of samples.

20. TYPES OF SAMPLING
TYPES OF SAMPLING ARE AS FOLLOWS:
• Probability Sampling.
• Non-Probability Sampling.
“Let’s take a closer look at these two methods of sampling”.

21. PROBABILITY SAMPLING
• Probability sampling is a sampling technique where a researcher sets a
selection of a few criteria and chooses members of a population randomly.
All the members have an equal opportunity to be a part of the sample
with this selection parameter.
• Sampling technique in which researchers choose samples from a larger
population using a method based on the theory of probability. This
sampling method considers every member of the population and forms
samples based on a fixed process.
• FOR EXAMPLE, In a population of 1000 members, every member will
have a 1/1000 chance of being selected to be a part of a sample.
Probability sampling eliminates bias in the population and gives all
members a fair chance to be included in the sample.

22. TYPES OF PROBABILITY
There are four types of probability sampling techniques:
1. SIMPLE RANDOM SAMPLING.
2. CLUSTER SAMPLING.
3. SYSTEMATIC SAMPLING.
4. STARTIFIED RANDOM SAMPLING.

23. USES OF PROBABILITY STATISTICS
• Reduce Sample Bias: Using the probability sampling method, the
bias in the sample derived from a population is negligible to non-existent.
The selection of the sample mainly depicts the understanding and the
inference of the researcher. Probability sampling leads to higher quality
data collection as the sample appropriately represents the population.
• Diverse Population: When the population is vast and diverse, it is
essential to have adequate representation so that the data is not skewed
towards one demographic. For example, if Square would like to
understand the people that could make their point-of-sale devices, a
survey conducted from a sample of people across the US from different
industries and socio-economic backgrounds helps.

24. NON-PROBABILITY STATISTICS
• The non-probability method is a sampling method that involves a
collection of feedback based on a researcher or statistician’s sample
selection capabilities and not on a fixed selection process.
• The output of a survey conducted with a non-probable sample leads to
skewed results, which may not represent the desired target population.
But, there are situations such as the preliminary stages of research or
cost constraints for conducting research, where non-probability sampling
will be much more useful than the other type.

25. TYPES OF NON-PROBABILITY
STATISTICS
1. CONVINCING SAMPLING
2. JUDGMENTAL SAMPLING.
3. SNOWBALL SAMPLING.
4. QOUTA SAMPLING.

26. USES OF NON-PROBABILITY SAMPLING
• Create a hypothesis: Researchers use the non-probability sampling
method to create an assumption when limited to no prior information is
available. This method helps with the immediate return of data and
builds a base for further research.
• Exploratory research: Researchers use this sampling technique widely
when conducting qualitative research, pilot studies, or exploratory
research.
• Budget and time constraints: The non-probability method when there are
budget and time constraints, and some preliminary data must be
collected. Since the survey design is not rigid, it is easier to pick
respondents at random and have them take the survey or questionnaire.

27. DIFFERENCE BETWEEN PROBABILITY AND
NON-PROBABILITY SAMPLING
PROBABILITY SAMPLING
• Probability Sampling is a
sampling technique in which
samples from a larger population
are chosen using a method based
on the theory of probability.
• Random sampling method.
• The population is selected
randomly.
• Since there is a method for
deciding the sample, the
population demographics are
conclusively represented.
NON-PROBABILITY
SAMPLING
• Non-probability sampling is a
sampling technique in which the
researcher selects samples based
on the researcher’s subjective
judgment rather than random
selection.
• Non-Random sampling method.
• The population is selected
arbitrarily.
• Since the sampling method is
arbitrary, the population
demographics representation is
almost always skewed.

28. THANKYOU
MADE BY:
GHAYUR KHALID WERYAH (053)
GREY SECTION