What is Sampling in research and sampling methods in research?

Sampling-representation

SAMPLING:

 Sampling method is the act , process , or technique of selecting a suitable sample ,or a representative Part of population for the purpose of determining Parameter or characteristics of the whole population.

Sampling-representation

Sample-ing is most important in a chemical analysis. Chemical analysis use only a small fraction of the available sample. The fraction of soil is collected for analysis must be representative of the bulk material.       

    A chemical analysis is most often performed on only a small fraction of the material   of interest,  for example a few milliliters of water from a polluted lake.The composition of this fraction must reflect as closely as possible the average composition of the bulk of the materials if the results are to be meaningful . 

Representative fraction:

The process by which a representative fraction is acquired is termed sampling. Often sampling is the most difficult step in the entire analytical process and the step that limits the accuracy of the procedure. This statement is especially true when the material to be analyzed is a large and in homogeneous liquid , such as a lake , or an in homogeneous solid, such as an ore , a soil ,or a piece of animal tissue.

Sampling for a chemical analysis necessarily requires the use of statistics because conclusions will be drawn about a much larger amount of material from the analysis of a small laboratory sample. From the observation of the  we use statistics , such as the mean and standard deviation , to draw conclusions about the population. The literature on sampling is extensive’; we provide only a brief introduction in this section.

Obtaining a Representative Sample:

 The sampling process must ensure that the items chosen are representative of the bulk of material or population. The items chosen for analysis are often called sampling units or sampling increments.

Example sampling :

For example , our population might be 100 coins, and we might wish to know the average concentration of lead in the collection of coins. Our sample is to he composed of 5 coins. Each coin is a sampling units or increment. In the statistical sense, the sample corresponds to several small parts taken from different  parts of the bulk material. To avoid confusion , chemists usually call the collection  of sampling unit or increment the gross sample.

laboratory sample:

For analysis in the laboratory , the gross sample is usually reduced in size and homogenized to create the laboratory sample. ln some cases , such as sampling powders, liquids , and gasses,we do not have obvious discrete items. Such materials may not he homogeneous because  they may consist of microscopic particles of different composition or, In the case of fluids,zones where concentrations of the analyte differ. With these materials, we can prepare a representative sample by taking our sample incitement from different regions of the bulk material. The three steps that are usually involved in obtaining the laboratory sample. 

Step I is often straightforward with the population being as diverse as a carton of bottles containing vitamin tablets , a field of wheat , the brain of rat or the mud from a stretch of river bottom. 

Steps 2 and 3 are seldom simple and may require tremendous effort and ingenuity.

Statistically , the goals of the sampling process are:

 1. To obtain a mean analyte concentration that is an unbiased estimate of the population mean. This goal can he realized only if all members of the population have an equal probability of being included in the sample.

 2. To obtain a variance in the measured analyte concentration that is an unbiased estimate of the population variance so that valid confidence limit can be found for the mean , and various hypothesis test: can be applied. This goal can be reached only if every possible sample is equally likely to be drawn. 

Both goals require obtaining a random sample. Here the term tandem sample does not imply that the samples are chosen in a haphazard manner. Instead a randomization procedure is applied to obtain such a sample.

Sampling Example:

 For example, suppose our sample is to consist of 10 pharmaceutical tablets to be drawn from 1,000 tablets off a production line. One way to ensure the sample is random is to choose the tablets to be tested from a table of random numbers.Here, we would assign each of the tablets a number from 1 to 1000 and use the sorted random numbers in column C of the spreadsheet to pick tablet 16, 33, 97, etc. for analysis.

  PURPOSE:

  • ECONOMY :

                   Tacking a sample requires a fewer resources than a census.

  • TIMELINESS:   

                 A sample may provide you with needed Information quickly.

  • INACCESSIBILITY OF SOME OF POPULATION:

              There are some population that are so difficult to get access to that only a sample can be used.

   BIAS AND ERROR IN SAMPLING


                                         A sample is expected to mirror the population from which it comes,however, there is no Guarantee that any sample will be precisely representative of the population from which it comes.


 
   SAMPLING METHOD

  •      ASAP long method mean how a sample is selected from given population.
  • The large the number of units observed For data collection, the more representative is the sample of its population.
  • The sample method employed for selecting a sample is important in determining how Closely the sample represents the population.


 RANDOM SAMPLING:

  *simple random sampling

*stratified random sampling

  1. Systemic sampling
  2. Multistage sampling
  3. Cluster sampling

In this method ,the sample is being selected in such a way that each unit of the population has an equal chance of being included in sample.



Two types of simple random Sampling method


*lottery method

*random number tables

    LOTTERY METHOD

SIMPLE method of selecting a random method:

  1. Suppose we have 500 units in a population and we wish to select 50 units in them.
  2. So assign the number 1 to 500 units of population Prepare slips bearing numbers 1 to 500.
  3. The slips should be homogeneous in shape ,size size ,co-lour etc.
  4. These slips are shuffled and put in a box.
  5. 50 slips are selected .
  6. The unit with the number on slips selected will constitute a random sample.
      RANDOM NUMBER TABLES:
  • Used as a device to choose samples which include in survey, a quality control inspection sample, or to assign experimental units to treatment such as assigning patients to drug treatment.
  • It is most practical and inexpensive method of selecting a random method.

  Systematic sampling:


            This technique is used when complete and up to date list is available in the units on population.

MULTISTAGE SAMPL-ING:


                       THIS METHOD is useful in many large scale surveys where the preparation of the list of all units is difficult.



CLUSTER SAMPLING:


           IN THIS method , population from which the sample is to be drawn is divided into numbers of groups or clusters each of which contain “sub units”.

The clusters may or may not have equal numbers of units.

  1.   SAMPLING PLANS
  2. SINGLE SAMPLING PLAN
  3. DOUBLE SAMPLING PLAN

SINGLE SAMPLING PLAN:-

  •   INSPECT a sample “n” place from “N”.
  • If the number of defects founds in sample doesn’t Excess “c” the lot is accepted.

DOUBLE SAMPLING PLAN:-


  In this sampling :

  • After test three condition arises
  • Accept lot
  • Reject lot
  • No decision: in the case second sample is taken and the two combine results of both the sample and made final decision.

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