Research Methodology - Questionnaire & Sampling
LAW OF INERTIA OF LARGE NUMBERS - STATISTICAL LAWS
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The Law of inertia of Large Numbers is an immediate deduction from the Principle of Statistical Regularity.
LAW OF
INERTIA OF LARGE NUMBERS
The Law of inertia of Large Numbers is an immediate deduction from the Principle of Statistical Regularity. Law of Inertia of Large Numbers states, “Other things being equal, as the sample size increases, the results tend to be more reliable and accurate.” This is based on the fact that the behavior or a phenomenon en masse. I.e., on a large scale is generally stable. It implies that the total change is likely to be very small, when a large number or items are taken in a sample. The law will be true on an average. If sufficient large samples are taken from the patent population, the reverse movements of different parts in the same will offset by the corresponding movements of some other parts.
In a sample survey, since only a small portion of the population is studied its results are bound to differ from the census results and thus, have a certain amount of error. In statistics the word error is used to denote the difference between the true value and the estimated or approximated value. This error would always be there no matter that the sample is drawn at random and that it is highly representative. This error is attributed to fluctuations of sampling and is called sampling error. Sampling error exist due to the fact that only a sub set of the population has been used to estimate the population parameters and draw inferences about the population. Thus, sampling error is present only in a sample survey and is completely absent in census method.
Sampling
errors occur primarily due to the following reasons:
Some of the bias is introduced by the use of defective sampling technique for the selection of a sample e.g. Purposive or judgment sampling in which the investigator deliberately selects a representative sample to obtain certain results. This bias can be easily overcome by adopting the technique of simple random sampling.
When difficulties arise in enumerating a particular sampling unit included in the random sample, the investigators usually substitute a convenient member of the population. This obviously leads to some bias since the characteristics possessed by the substituted unit will usually be different from those possessed by the unit originally included in the sample.
Bias due to defective demarcation of sampling units is particularly significant in area surveys such as agricultural experiments in the field of crop cutting surveys etc. In such surveys, while dealing with border line cases, it depends more or less on the discretion of the investigator whether to include them in the sample or not.
Sampling method consists in estimating the parameters of the population by appropriate statistics computed from the sample. Improper choice of the estimation techniques might introduce the error.
Sampling error also depends on the variability or heterogeneity of the population to be sampled.
Sampling errors are of two types: Biased Errors and Unbiased Errors
The errors that occur due to a bias of prejudice on the part of the informant or enumerator in selecting, estimating measuring instruments are called biased errors. Suppose for example, the enumerator uses the deliberate sampling method in the place of simple random sampling method, then it is called biased errors. These errors are cumulative in nature and increase when the sample size also increases. These errors arise due to defect in the methods of collection of data, defect in the method of organization of data and defect in the method of analysis of data.
Errors which occur in the normal course of investigation or enumeration on account of chance are called unbiased errors. They may arise accidentally without any bias or prejudice. These errors occur due to faulty planning of statistical investigation.
To avoid these errors, the statistician must take
proper precaution and care in using the correct measuring instrument. He must
see that the enumerators are also not biased. Unbiased errors can be removed
with the proper planning of statistical investigations. Both these errors
should be avoided by the statisticians.
Errors in sampling can be reduced if the size of sample is increased. This is shown in the following diagram.
From the above diagram it is clear that when the size of the sample increases, sampling error decreases. And by this process samples can be made more representatives to the population.
The Law of inertia of Large Numbers is an immediate deduction from the Principle of Statistical Regularity. Law of Inertia of Large Numbers states, “Other things being equal, as the sample size increases, the results tend to be more reliable and accurate.” This is based on the fact that the behavior or a phenomenon en masse. I.e., on a large scale is generally stable. It implies that the total change is likely to be very small, when a large number or items are taken in a sample. The law will be true on an average. If sufficient large samples are taken from the patent population, the reverse movements of different parts in the same will offset by the corresponding movements of some other parts.
Sampling Errors:
In a sample survey, since only a small portion of the population is studied its results are bound to differ from the census results and thus, have a certain amount of error. In statistics the word error is used to denote the difference between the true value and the estimated or approximated value. This error would always be there no matter that the sample is drawn at random and that it is highly representative. This error is attributed to fluctuations of sampling and is called sampling error. Sampling error exist due to the fact that only a sub set of the population has been used to estimate the population parameters and draw inferences about the population. Thus, sampling error is present only in a sample survey and is completely absent in census method.
Faulty selection of the sample:
Some of the bias is introduced by the use of defective sampling technique for the selection of a sample e.g. Purposive or judgment sampling in which the investigator deliberately selects a representative sample to obtain certain results. This bias can be easily overcome by adopting the technique of simple random sampling.
Substitution:
When difficulties arise in enumerating a particular sampling unit included in the random sample, the investigators usually substitute a convenient member of the population. This obviously leads to some bias since the characteristics possessed by the substituted unit will usually be different from those possessed by the unit originally included in the sample.
Faulty demarcation of sampling units:
Bias due to defective demarcation of sampling units is particularly significant in area surveys such as agricultural experiments in the field of crop cutting surveys etc. In such surveys, while dealing with border line cases, it depends more or less on the discretion of the investigator whether to include them in the sample or not.
Error due to bias in the estimation method:
Sampling method consists in estimating the parameters of the population by appropriate statistics computed from the sample. Improper choice of the estimation techniques might introduce the error.
Variability of the population:
Sampling error also depends on the variability or heterogeneity of the population to be sampled.
Sampling errors are of two types: Biased Errors and Unbiased Errors
Biased Errors:
The errors that occur due to a bias of prejudice on the part of the informant or enumerator in selecting, estimating measuring instruments are called biased errors. Suppose for example, the enumerator uses the deliberate sampling method in the place of simple random sampling method, then it is called biased errors. These errors are cumulative in nature and increase when the sample size also increases. These errors arise due to defect in the methods of collection of data, defect in the method of organization of data and defect in the method of analysis of data.
Unbiased Errors:
Errors which occur in the normal course of investigation or enumeration on account of chance are called unbiased errors. They may arise accidentally without any bias or prejudice. These errors occur due to faulty planning of statistical investigation.
Reducing Sampling Errors:
Errors in sampling can be reduced if the size of sample is increased. This is shown in the following diagram.
From the above diagram it is clear that when the size of the sample increases, sampling error decreases. And by this process samples can be made more representatives to the population.
Tags : Research Methodology - Questionnaire & Sampling
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