Sampling MBA I

7/29/2019 Sampling MBA I

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Sampling

MBA -I


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Sample: A small part representing the full

population or universe.Importance(advantages)

Economical

Saves time Saves work force

Testing of accuracy-results of two samples

can be compared. Only method in many cases.


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Disadvantages(demerits):

Can mislead result.(if survey is notproperly done).

Need specialised knowledge. Not useful in hetrogeneous units.

Impossibility of sampling(when population

is very small)


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Methods of sampling

1. Random sampling- also known as chance orprobability sampling as each unit has equalchance of being selected.

Types of random sampling:-

a) Simple random sampling-practically simplerandom sampling is known as randomsampling.Example:

Lottery Method Rotating the drum- contains wooden square

pieces numbering 0 ,1,29.


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b) Restrictive random sampling-(those random

sampling which has some restrictions)Example:

Stratified random sampling:total no of units ofpopulation is divided into groups or strata and units

are picked from these groups. Systematic random sampling:-units are arranged

in some systematic way like

alphabets,numericals,etc and then units of sampleare selected with definite sequence and equaldiatances.


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c)Multi stage random sampling:-

samples are selected at every stage and ateach stage random sampling is used.

d)Cluster sampling:-

total population is divided in to clusters(groups) and simple random sample isdrawn from each cluster.


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2. Non-random sampling- does not provide

equal chance to each unit of populationin selection.

Types of non-random sampling:

a. Purposive sampling- investigatorsselects the units according to his ownchoice and requirements.

b. Qouta sampling- investigators fixescertain qouta and then selection is donefrom these qoutas.


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c). Convenience Sampling- sample units

are selected at the convenience of theinvestigator e.g using telephonedirectory,using government records.etc for

selecting units of sample.d). Extensive sampling-only those units are

ignored which are difficult to collect.


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Sampling theory

A study concern with population andsamples drawn from population.

Objectives of sampling theory:

1.Study of population characteristics.

2.Hypothesis testing.


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Sampling and non sampling errors

Sampling errors: difference between sampleresult and population result caused mainly byfaulty selection of samples .

Non-sampling error:

causes: Incomplete investigation. Printing errors. Faulty questions in questionnaire. Calculations mistakes. Incomplete investigation,etc.


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Parameter and statistic


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Sampling Distribution

The frequency distribution which is formedwith different values of statistic(like mean,median,standard deviation,etc)computed

from different samples of equal size drawnfrom the same population .

It has two properties

1.It is equal to Normal Distribution.

2.Equity of mean


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Concept of Standard Error

When variation of observation of asampling distribution is calculated it iscalled as standard error. Therefore the

standard deviation of sampling distributionis known as standard error of a statistic.


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Hypothesis testing

Hypothesis :- an assumption or statementabout population.

Hypothesis testing:- a procedure thatdecides whether to accept the hypothesisor not by analysing the informationobtained from the sample.


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General procedure for testing thehypothesis

1. Statement of the problem :- problem is clearly stated or definedi.e. whether decision is taken to accept the hypothesis or to rejectit or it is to be taken in respect of difference between sample orpopulation.

2. Setting up a hypothesis:- here a hypothesis is set up(nullhypothesis & alternative hypothesis) ; common way of formulating

the hypothesis is that there is no difference between samplemean and population mean.

example:mean salary of employees of the company andmean salary of sample of 50 employees from the same company is

equal will be written asHo: = x Ho:-Null hypothesis

:-population meanx :-sample mean


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As against null hypothesis there will be

altenate hypothesis(H) which challengesthe null hypothesis.

Example: H: x H Alternate hypothesis:- population mean

x:-samplemean

or H : < x

or H : > x


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3.Applying test :- this step uses various test tocheck the hypothesis.We use different test for

large and small samples.4. Level of significance:- this step checks the

confidence level .Generally confidence is

checked at 1% and 5% level of significance.1% level of significance means 99% confidence.

5% level of significance means 95% confidence .


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5.) critical values: these are standardvalues obtained from specific tables at aparticular level of significance from which

test values are compared.

6.) Interpretation: in this method finaldecision is taken by comparing test valuesfrom critical value.


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Errors in hypothesis

Type I error:

when null hypothesis is true but it isrejected .

Type II error :

when null hypothesis is false but it is

accepted.

Sampling MBA I

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