such all the characteristics of the population may not be present in the sample drawn from the same population. The errors involved in the collection, processing and analysis of the data may be broadly classified into two categories namely, (i) Sampling Errors (ii) Non-Sampling Errors (i) Sampling Errors Errors, which arise in the normal course of investigation or enumeration on account of chance, are called sampling errors. Sampling errors are inherent in the method of sampling. They may arise accidentally without any bias or prejudice.
Sampling Errors arise primarily due to the following reasons: (a) Faulty selection of the sample instead of correct sample by defective sampling technique. (b) The investigator substitutes a convenient sample if the original sample is not available while investigation. (c) In area surveys, while dealing with border lines it depends upon the investigator whether to include them in the sample or not. This is known as Faulty demarcation of sampling units.
(ii)Non-Sampling Errors The errors that arise due to human factors which always vary from one investigator to another in selecting, estimating or using measuring instruments( tape, scale)are called Non-Sampling errors.It may arise in the following ways: (a) Due to negligence and carelessness of the part of either investigator or respondents. (b) Due to lack of trained and qualified investigators. (c) Due to framing of a wrong questionnaire. (d) Due to apply wrong statistical measure (e) Due to incomplete investigation and sample survey.
. . Sampling distribution Definition . Sampling distribution of a statistic is the frequency distribution which is formed with various values of a statistic computed from different samples of the same size drawn from the same population.
XII Std - Business Maths & Stat EM Chapter - - For instance if we draw a sample of size n from a given finite population of size N, then the total number of possible samples is N N n N k ! !( )! (say). For each of these k samples we can compute some statistic, t t x x x x n = ( ,...
), in particular the mean x