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Need of Statistics

To Understand the Literature

One can read much of the literature without encountering statistical concepts, methods and techniques.
Without proper understanding of statistics it is very difficult to interpret and digest the results mentioned in research articles, theses, dissertations etc.
With the results the purpose of the articles may not be clear to the reader, therefore, he/ she loses interest in reading.

To Fabricate the Research Problems

There is a much difference between an abstract and rational thinking.
A researcher need to know in advance about his/ her feasible hypothesis which he/ she wishes to verify.
He/ she must be aware of proper statistical design ad techniques which are possible to implement in his/ her research problem.
Thus, it is possible to fabricate the rationally correct research problem.

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To Develop Scientific Temper

Statistics is important in order to facilitate the researchers to think rationally about any situation.
During a match a player’s decision for any move is purely based on his/ her rational thinking.
A coach visualizes the training schedule of his/ her trainees based upon his/ her decision derived from his/ her previous experiences.
All this is possible only if one observes any fact justifiable as per the scientific arguments.

To assess the authenticity of Research

Several researchers publish their findings bases upon their research work.
In order to assess the authenticity of their statement one can read their research report.
But to prove the conformity between their statements and the actual fact one should be able to understand the statistical techniques used in report for drawing conclusions.

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To Contradict the Unjustifiable Claims

Many companies make a claim about their product by using research findings of the scientists.
These claims may be tested by conducting an experiment under the required condition and analyzing the data.
Thus knowledge of designing an experiment and various statistical techniques are essential to write off the unjustifiable claims.

To Develop the Indices on Various Characteristics & Performances

In order to assess the academic excellence of a student, his/ her performance in the examination is used as an index.
Similarly, to assess the general fitness of a college student an index is required.
There are several means and methods are suggested by various authors to measure various characteristics of an individual and for the measurement of performance on different events in sports.
but it is required lot of statistical concepts to further improve the quality of these indices.

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To Develop Norms on various traits

Performance on any trait like sit-ups, pull-ups etc. needs to be converted into a score by using a scale ranging from 0-100, to motivate a student.
Conversion of such performance is known as norms.
Such norms are easily understood by a common man and can be used in the admission procedure of the student in the schools/ colleges/ universities.

To Conduct Research

Statistical concepts and techniques are important;

in designing an experiment
in drawing a representative sample
in administrating the test
in choosing the correct statistical test for conducting research
in interpreting the results

Thus, it is extremely important to have an appropriate knowledge of statistical concepts, methods and techniques to conduct a research in an accurate manner for drawing the reliable conclusions.

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Importance of Statistics

Using appropriate statistical design reduces the overall error in the experiment giving more reliable conclusions.
New techniques in different sports could be tested with the existing one for better results by using statistical techniques.
Helps in the development of new techniques and strategies.
Helps in the development of test battery such as for measurement of motor fitness, athletic fitness, general and specific fitness.
Helps in deciding the optimum training load.
Helps in construction and development of questionnaire.
Helps in finding out relationship between independent and dependent variable.

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Types of Statistical Process

Descriptive Statistic

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way.
Descriptive statistic are simply a way to describe our data.
Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of data.
Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data.

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Comparative Statistic

Comparative statistic is helpful in those situations where one want to have comparison between two or more groups
Comparative statistic facilitate one in knowing which one is better than another.
Like if one wants to now difference between two groups then t-test will be used and if having more than three groups then Analysis of Variance (ANOVA) will be employed.

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Inferential Statistic

Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn. It is, therefore, important that the sample accurately represents the population.
Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates.
we can take the results of an analysis using a sample and can generalize it to the larger population that the sample represents.

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Predictive Statistic

Predictive statistic is about making prediction about future form the observed data.
Predictive statistic included regression analyses and its related tests

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Attribute

Attribute may be defined as qualitative characteristics of an individual.
It can not be graded.
Eg. Athlete v/s non athlete
Attribute can be classified as two class attribute, three class attribute and so on depending upon how many classes an attribute is classified.
Eg. Two class attribute - If a person is classified as obese and non- obese.
Eg. Three class attribute – according to performance level like beginner, advance and elite

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Variable

Variable is a quantitative phenomena of any trait of an individual which keeps on changing with individual, time or place.
It can be graded according to its magnitude.
Eg. Height or weight of an athlete
Variable is normally repersented by x,y,z or any alphabet.
Further , the variable is classified into two categories

Continuous - A variable which can assume any order of fractional values within a range such as height, weight, speed score etc.
Discrete – A variable is said to be discrete if it takes only whole number such as score in basketball, goal in football etc.

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Raw Score

In general words, during collection of data the scores which we collect in form of quantitative or qualitative are named as raw scores.
Any amount of quantitative or qualitative scores obtained as a result of measurement or due to an experiment are termed as raw scores, provided no statistical treatment has been done on it
Eg.

Age 15 16 17 18 19 20 16

Push ups 10 12 15 16 18 20 22

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Single Score

When scores are not a whole number it is called as continuous score.
Eg., 6.4, 9.5, 4.7, 74.36 etc.
If these scores are approximated to its nearest whole number, it is said to be as a single score.
Eg.
6.4 ~ 6
9.5 ~ 10
4.7 ~ 5
74.36 ~ 74

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Population

Population may be defined as collection of all those elements about which certain decisions are to be made.
Eg. Athlete of national level, basketball players of university level.
Population is the collection of units having similar properties under study.

Sample

A small portion of population units is a sample.
Sample is the subset of population.
It must posses the characteristics of population.
It should be true representative of population, only then the results can be easily generalized.

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Parameter

In order to study the nature of population we require some kind of indices which measure various dimensions of characteristics.
Parameter are statistical constants which are used to define the nature of the population.
Mean of population - µ
Standard deviation of population – σ

Statistic

Indices used to measure the characteristics of sample is known as statistics.
Statistics could be defined as the function of sample observations and are represented by x and s
Mean - x
SD - S

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Types of Data

Any information which is gathered in form of qualitative or quantitative form is said to be as data.
Data is of two types:-

Quantitative/ Parametric/ Metric
Qualitative/ Non- Parametric/ Non-Metric
Quantitative/ Parametric/ Metric
Interval
Ratio
Qualitative/ Non- Parametric/ Non-Metric
Nominal
Ordinal

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Ratio

Interval

Ordinal

Nominal

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Nominal

The name 'Nominal' comes from the Latin nomen, meaning 'name' and nominal data are items which are differentiated by a simple naming system.
Nominal items may have numbers assigned to them and this may appear ordinal but it is not .
Measurement at its weakest level exists when numbers or other symbols are used simply to classify an object, person, or characteristic. When numbers or the other symbols are used to identify the group to which various objects belongs, these numbers or symbols constitute a nominal or categorical scale.
Nominal items are usually categorical, in that they belong to a definable category, such as male, female, sports person etc.
Eg. Chest number of jersey.

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Ordinal

Items on an ordinal scale are set into some kind of order by their position on the scale.
This may indicate such as temporal position, superiority, etc.
The order of items is often defined by assigning numbers to them to show their relative position.
Letters or other sequential symbols may also be used as appropriate.
Ordinal items are usually categorical, in that they belong to a definable category.
Data has order, but does not have a numerical scale
eg. Elite Athlete High Performer Beginner

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Interval

Interval data is measured along a scale in which each position has equal distance from one another.
This allows for the distance between two pairs to be equivalent in some way.
The number zero has no meaning.
Eg. 100 mt track lanes

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Ratio

In a ratio scale, numbers can be compared as multiples of one another. Thus one person can be twice as tall as another person.
Here the number zero has a meaning.
Eg. Age - the difference between a person of 35 and a person 38 is the same as the difference between people who are 12 and 15. A person can also have an age of zero.
Eg. Temperature
Interval and ratio data measure quantities and hence they are known as quantitative data.
Because they can be measured on a scale, therefore, they are also called scale data.