lecture 9; sampling techniques by Dr. Salma Amir

1. Lecture No. 09Course title: Fundamental Analytical ChemistryTopic: Sampling Techniques Course instructor: Dr. Salma Amir GFCW Peshawar

2. Representativeness of sample • Representativeness: When sampling method is adopted by the researcher, the basic assumption is that the samples so selected out of the population are the best representative of the population under study. Thus good samples are those who accurately represent the population. Probability sampling technique yield representative samples.

3. Factors affecting sample representativeness • the target population • the sampling method/procedure • the sample size • the sampling plan

4. 1. Target population to be sampled A. Homogenous material: When the material being sampled is homogeneous, individual samples can be taken from any part of the material without regard to possible sampling errors. E.g., sampling of homogenous liquid B. Heterogeneous material: When the target population’s heterogeneity is of concern, then the sample collected from different parts of material. If the target population can be thoroughly homogenized, then samples can be taken without introducing sampling errors.

5. 2. Sampling plan • The sampling plan is the strategy employed to represent the distribution of one or multiple analytes in the object of study.  Five questions should be considered when designing a sampling plan: 1. From where within the target population should samples be collected? 2. What type of samples should be collected? 3. What is the minimum amount of sample needed for each analysis? 4. How many samples should be analyzed? 5. How can the overall variance be minimized?

6. 3. Sample size Size: A good sample must be adequate in size and reliable. The sample size should be such that the inferences drawn from the sample are accurate to a given level of confidence to represent the entire population under study. The size of sample depends on number of factors. Some important among them are: 1 Homogeneity or Heterogeneity of the population:Selection of sample depends on the nature of the population. It says that if the nature of population is homogeneous then a small sample will represent the behavior of entire population. This will lead to selection of small sample size rather than a large one. On the other hand, if the population is heterogeneous in nature then samples are to be chosen as from each heterogeneous unit. 2. Number of classes proposed: If a large number of class intervals to be made then the size of sample should be more because it has to represent the entire population. In case of small samples there is the possibility that some samples may not be included.

7. Nature of study:The size of sample also depends on the nature of study. For an intensive study which may be for a long time, large samples are to be chosen. Formula for Size of sample where ns is the number of samples and ss is the sampling standard deviation. Rearranging and substituting e for the quantity (μ-X)

8. 4. Sampling methods/techniques • The sampling method outlines the way in which the sample units are to be selected. The choice of the sampling method is influenced by the objectives of the analysis, availability of financial resources, time constraints, and the nature of the problem to be investigated. • The sampling techniques can be classified as • Random Sampling • Judgmental sampling • Systematic sampling • Systematic–judgmental sampling • Stratified sampling • Convenience sampling

9. 1. Random sampling A sample collected at random from the target population. The best method for ensuring the collection of a random sample is to divide the target population into equal units, assign a unique number to each unit, and use a random number table to select the units from which to sample. A randomly collected sample makes no assumptions about the target population, making it the least biased approach to sampling. On the other hand, random sampling requires more time and expense than other sampling methods since a greater number of samples are needed to characterize the target population. 2. Judgmental sampling Samples collected from the target population using available information about the analyte’s distribution within the population. Judgmental sampling is common when we wish to limit the number of independent variables influencing the results of an analysis. For example, a researcher studying the bioaccumulation of polychlorinated biphenyls (PCBs) in fish may choose to exclude fish that are too small or that appear diseased. Judgmental sampling is also encountered in many protocols in which the sample to be collected is specifically defined by the regulatory agency.

10. 3. Systematic sampling Samples collected from the target population at regular intervals in time or space. To find the distribution of dissolved O2 in a lake, samples can be systematically collected by dividing the system into discrete units using a two- or three-dimensional grid pattern. Samples are collected from the center of each unit, or at the intersection of grid lines. When a heterogeneity is time-dependent, as is common in clinical studies, samples are drawn at regular intervals. 4. Systematic–judgmental sampling A sampling plan that combines judgmental sampling with systematic sampling.

11. 5. Stratified sampling A sampling plan that divides the population into distinct strata from which random samples are collected. Many target populations are conveniently subdivided into distinct units, or strata. For example, in determining the concentration of particulate Pb in urban air, the target population can be subdivided by particle size. In a stratified sampling the target population is divided into strata, and random samples are collected from within each stratum. Strata are analyzed separately, and their respective means are pooled to give an overall mean for the target population 6. Convenience sampling A sampling plan in which samples are collected because they are easily obtained. In convenience sampling, sample sites are selected using criteria other than minimizing sampling error and sampling variance. In a survey of groundwater quality, for example, samples can be collected by drilling wells at randomly selected sites, or by making use of existing wells. The latter method is usually the preferred choice. In this case, cost, expedience, and accessibility are the primary factors used in selecting sampling sites.