Introduction:

Sampling is a statistical technique which is used in almost every field in order to collect information and on the basis of this information inference about the characteristic of the population are made. The values of population characteristic or summarised by certain numerical descriptive measures, called parameters. the values of the population parameters, which are in most situations are noun would have to be estimated and to get estimates, we Resort to sampling. The observation composing a sample are used to calculate corresponding numerical descriptive measures, call statistic. We use statistics to estimate parameters. considerations Of time and cost or other reasons for sampling. Prior to introducing some of the most commonly used sampling methods, we proceed to some definitions and to a brief description of the basic concept involved in sampling.

Statistical populations:

A statistical population for the universe is defined as the aggregate or total of all individual members are objects, whether animate or inanimate, concrete or abstract, of some characteristic of interest. the individual members of the population are called sampling units are simply units. Sampling the unit from which information is required, maybe a college College student, an animal, a tree, a household, a block, a town, a small area, field, business etc. A set of n sampling units and the process of selecting a sample is known as sampling. the numerical values assigned to units of interest and created as values of a random variable x, and the distribution of x is called the population distribution. A population can be either finite or infinite depending upon whether its contents of a countable or uncountable number of units. The population of all students in a college, the population of all licensed motor drivers, The population of all horses in our country, etc. examples of a finite population. the total number of units in a finite population is called the sample of the population and is denoted by N. the examples of infinite populations are the population of all points on a line, the population of pressures at various point in the atmosphere, Etc. the population of concrete units such as trees, households, students, etc. e is called an existent population while a hypothetical population consists of all conceivable ways in which an event can occur,e.g all possible throws of a die. such a population does not exist in concrete manners but it is only to be thought of.

Furthermore, the assembled population is a date from which a sample is chosen, whereas a population about which we wish to draw inferences, is called a target population. The following two examples may suffice to illustrate the difference between a sample population and the target population.

Suppose we desire to know the opinions of college students in the province of Punjab with regards to the present examination system. Then our population will consist of the total number of student in all the colleges in the province. suppose an account of the storage of resources or time, we are able to conduct such a survey only on 5 causes squirted throughout the province, say, situated in the large urban areas. in such a case, the target population consists of the student of all the colleges in the province, while on the other hand, the sample and population consist of the students of 5 colleges, from which the sample of students will be selected. as long as a student of these 5 colleges is representative of the student of all colleges, the result would be applicable to all the colleges. similarly, the sample and population may consist of patients In district hospitals and the target population may consist of the total number of patients in the province. it is of some importance to emphasize dad the sample and population should be such results cannot be extended to the target population, they hold good for the sample and population. a population is discrete when the number of units comprising the population is countable, otherwise, it is continuous.

Advantages of sampling:

The important advantages of sampling over the whole list are briefly stated below:

1. Sampling saves money as it is much economical to collect the wanted information from a small sample than from the full population.

2. Sampling saves A lot of time and energy as the needed data are collected and proceed much faster than census information. And this is a very important consideration in all types of Investigations or surveys.

3. Sampling provides information that is almost as accurate as their opinion from a complete census, rather a properly designed and carefully executed sample the survey will provide more accurate results. moreover, owing to reduce the volume of work, persons of a higher calibre and properly trained can be employed to analyze that data.

4. Sampling makes it possible to obtain more detailed information from each unit of the samples are as collecting data from a new unit of the population can be completer and more thorough.

5. Sampling is essential to obtaining the date of when the measurement processes physical damages or destroys the sampling unit under investigation. For example, in order to measure the average lifetime of light bulbs, the measurement process destroys the sampling unit, the bulbs, as there are used until they burn out. A manufacturer will, therefore, use only a sample of light bulbs for this purpose and will not burn out all the bullets produced.

Similarly, the whole pot of soup cannot be tasted to determine if it has an acceptable flavour.

Probability and Non-probability sampling:

Sampling methods are broadly classified as probability sampling and nonprobability sampling. When each unit in a population has a known zero probability of its being included in the sample, the sampling is said to be probability sampling. A probability sampling is also called random sampling .the major types of probability sampling or simple random sampling, stratified random sampling, systematic sampling, cluster sampling, etc. The advantages of all probability sampling are that it provides an estimate of sampling error. probability sampling is widely used in various areas such as industrial agriculture business etc.

A non-probability sampling also called non-random sampling, is a process in which the personal judgment determines which unit of the population is selected from a sample. The disadvantages of non-probability sampling are that reliability of sample results cannot be determined in term of probability. Non-probability sampling techniques include purposive sampling and quota sampling.

Sampling with and without replacement:

Samples may be selected with a replacement or without replacement. sampling is said to be with replacement van from a finite population of a sampling unit is drawn, absorbed and then returned to the population before and after the unit is drawn. The population, in this case, Remains the Same and the sampling unit might be selected more than once. If on the other hand, the sampling unit is chosen and not returned to the population after it has been observed, the sampling is said to be without replacement.