Unit 1: Introduction – Meaning and Scope
The Word statistics have been derived from Latin word “Status” or the Italian word “Statista”, meaning
of these words is “Political State” or a Government. Shakespeare used a word Statist is his drama
Hamlet
By the 18th century, the term "statistics" designated the systematic collection of demographic and
economic data by states. For at least two millennia, these data were mainly tabulations of human and
material resources that might be taxed or put to military use. In the early 19th century, collectio n
intensified, and the meaning of "statistics" broadened to include the discipline concerned with the
collection, summary, and analysis of data.
Statistics stands for numeric information from which conclusions can be drawn. Statistical methods on
the other hand refers to methods used to collect, present, analyze and interpret numerical information.
According to Horace Secrist, “Statistics may be defined as the aggregate of facts affected to a marked
extent by multiplicity of causes, numerically expressed, enumerated or estimated according to a
reasonable standard of accuracy, collected in a systematic manner, for a predetermined purpose and
placed in relation to each other”2. This definition is both comprehensive and exhaustive.
Characteristics of Statistics
1. Statistics are aggregate of facts: Statistics are aggregate of facts. A single figure cannot called
statistics. Because can be drawn about such figures e.g. if we say that income of a person is RS
500, this figure is meaningless, because we cannot draw any conclusion about this figure i.e.
whether the income of a person has increased or decrease. Thus the science of statistics is the
science of aggregate and not of individuals.
2. Statistics are affected by multiplicity of causes: Statistics (i.e. figure) obtained about a particular
phenomena are due to a number of causes e.g. production of wheat depends on seeds,
fertilizer, irrigation, rainfall etc. it is not possible to study the influence of these factors
separately, because these factors jointly determine the yield.
3. Statistics are numerically expressed: Statistics are numerical statement of facts. Qualitative
expression such as honestly, good, bad etc. do not form statistics e.g. consider the following
statement “QUAID AZAM was a great leader”. This is not a statistics statement. If on the other
hand, we say that per capita income in Pakistan in 1960 was RS 840 and RS 1400 in 1990. This is
of course a statistics statement as per capita incomes is expressed numerically and is
comparable.
4. Statistics are collected in a systematic manner: When statistics are collected in a systematic
manner, then they may give accurate result. If they are collected in a haphazard manner, then
the very purpose of collecting statistics will be damaged such statistics always leads to
misleading conclusions. Thus there must be trained investigators and proper organizations for
the collection of statistics.
5. Statistics are placed in relation to each other: Statistics are collected mainly for the purpose of
comparison. Data collected must be comparable and homogenous e.g. if we compare the
heights of persons with their income, it is not statistics, on the other hand if we compare the
ages of husbands with the ages of their wives. Then it is called statistics.
6. Statistics are collected for a per-determined purpose: The purpose for which statistics are to be
collected is always determined in advance. It enables the investigator to distinguish between
wanted and unwanted data. If the purpose is not determined in advance , then investigator may
collect the irrelevant data.
Croxton and Cowden Definition of Statistics
According to Croxton and Cowden, „Statistics is the science of collection, presentation, analysis and
interpretation of numerical data from logical analysis‟ The four different components of Statistics as per
Croxton and Cowden are shown in figure
Divisions of Statistics / Applied Statistics
The two main divisions of statistics are descriptive statistics and inferential statistics. Although they are
both unique in their own right, they are normally used in connection with one another as part of a wider
statistical analysis of a given set of data
Descriptive statistics simply describe data - hence the rather self-explanatory title they are given. They
include modes, means, ranges and frequencies which help demonstrate striking trends, similarities and
differences within a set of data or between multiple sets of data.
Inferential statistics require human inference - again, as the name would suggest. We can look at pieces
of descriptive data which show certain trends to draw conclusions based on the patterns displayed. This
can lead to further sampling, matching and experimentation to see if a set of data links in with another
prediction based on original descriptive data.
Biometry
Biostatistics (or biometry) is the application of statistics to a wide range of topics in biology. The science
of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy,
agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and
the interpretation of, and inference from, the results. A major branch of this is medical biostatistics,
which is exclusively concerned with medicine and health.
Functions of Statistics
1. Statistics simplifies mass data: The use of statistical concepts helps in simplification of complex
data. Using statistical concepts, the managers can make decisions more easily. The statistical
methods help in reducing the complexity of the data and consequently in the understanding of
any huge mass of data.
1. Statistics makes comparison easier: Without using statistical methods and concepts, collection
of data and comparison cannot be done easily. Statistics helps us to compare data collected
from different sources. Grand totals, measures of central tendency, measures of
dispersion, graphs and diagrams, coefficient of correlation all provide ample scopes for
comparison.
2. Statistics brings out trends and tendencies in the data: After data is collected, it is easy to
analyse the trend and tendencies in the data by using the various concepts of Statistics.
3. Statistics brings out the hidden relations between variables: Statistical analysis helps in drawing
inferences on data. Statistical analysis brings out the hidden rel ations between variables.
4. Decision making power becomes easier: With the proper application of Statistics and statistical
software packages on the collected data, managers can take effective decisions, which can
increase the profits in a business.
Limitations of Statistics
1. Statistics does not deal with qualitative data: Qualitative data deals with meanings while
quantitative data deals with numbers. Qualitative data describes properties or characteristics
that are used to identify things. Quantitative data describes data in te rms of quantity using the
numerical figure accompanied by measurement unit. Statistics deals only with quantitative data.
2. Statistics does not deal with individual fact: Statistical methods can be applied only to
aggregates of facts, because analysis and interpretation of data is highly difficult in case of
individual facts.
3. Statistical inferences (conclusions) are not exact: Statistical inferences are true only on an
average. They are probabilistic statements. For example, in case of data, which consists of
height of 200 male persons taken from a graduate school, the inferences so obtained may not
hold true for an individual male person in particular.
4. Statistics can be misused and misinterpreted: Lack of sufficient knowledge of statistical science
often leads to incorrect conclusions. Therefore, proper care must be taken while selecting
collection method and also in choosing appropriate statistical models. Increasing misuse of
Statistics has led to increasing distrust in Statistics.
5. Common men cannot handle Statistics properly: The field of Statistics is so vast that it needs
experience as well as skill to effectively understand and apply the statistical concepts and
models. Hence, only statisticians can handle statistics properly.
Role of Statistics in Business, Commerce and Economics
Statistical methods are used by businessmen in making decision, estimation and comparison. Also in the
business activities such as production, finance, sales, accounting, purchase, quality control, marketing
etc, statistical methods are extensively used. Statistical methods are used to forecast the future trends.
Statistical methods are useful in understanding economic problems and formulating economic policies.
Five year plans, savings, taxation, exports and imports are evaluated through statistical methods.
Econometrics is a branch of statistics which deals with application of statistical methods in the field of
economics.
The physical and natural sciences like astronomy, engineering, biometry and pharmaceuticals. Also in
agricultural science, statisticaas is widely used. Thus the famous saying is that ‘sciences without statistics
bear no fruit; statsitcs without sciences has no root’
Causes of Distrust
Distrust of statistics arises due to the direct or indirect influence of certain causes. These causes can be:
a)
b)
c)
d)
e)
Figures can be easily believed.
Ignoring the limitations of statstics
Misues of the figures
Inadequate samples
Lack of subject knowledge.
Remedies to Remove Distrust
1.
2.
3.
4.
5.
Need of caution
Statistical limitations should be taken into consideration.
Self restraint, that is self control in statsitcal fallacies.
Statistics must be used by experts
Analytical study of data before it is used.
Some Statistical Concepts
Population or Universe
The totality of all units or individuals in a survey is called population or universe. If the number of
objects in a population is finite then it is called finite population otherwise it is known as infinite
population.
Example: Number of people in an apartment is a finite population while number of people in an entire
continent is an infinite population.
Sample
A sample is a part or subset of the population. By studying the sample, you can predict the
characteristics of the entire population from where the sample is taken. The data that describes the
characteristics of a sample is known as statistic.
A characteristic which is numerically measurable is called a quantitative. Quantitative data is data
expressing a certain quantity, amount or range. Usually, there are measurement units associated with
the data, for example, the height of a person in meters.
A characteristic which is not numerically measurable is called a qualitative characteristic. Qualitative
data is data describing the attributes or properties that an object possesses.
Variable
In a population, some characteristics remain the same for all units and some others vary from unit to
unit. The quantitative characteristic that varies from unit to unit is called a variable. The qualitative
characteristic that varies from unit to unit is called an attribute.
A variable that assumes only some specified values in a given range is known as discrete variable. A
variable that assumes all the values in the range is known as continuous variable. For example, the
number of children per family and number of petals in a flower are examples of discrete variables. The
height and weight of persons are examples of continuous variables.