Explain probability and non-probability sampling techniques used for analysis of food samples.

Explain probability and non-probability sampling techniques used for analysis of food samples.-Sampling is a technique for choosing certain individuals or a small portion of the population in order to draw conclusions about the population as a whole and estimate its characteristics.

Two types of sampling methods are:-

1. Probability sampling:- Using an approach based on probability theory, the researcher selects samples from a broader population using the probability sampling methodology. A participant must be chosen at random in order for them to be taken into account as a probability sample.

By using this statistical technique, a sample is chosen from a population such that each person has a known, non-zero chance of being chosen. The most important prerequisite for probability sampling is that each member of your population has an equal and known chance of being chosen.

Explain probability and non-probability sampling techniques used for analysis of food samples.Probability sampling is the process of selecting a small sample of people at random from a large population and then predicting that all of their responses will be representative of the entire population.

Probability sample types :-

Simple Random Sampling: This technique entails selecting a sample from the population at random and without prejudice. It is the most fundamental and uncomplicated type of probability sampling.

· Stratified random Sampling: With this technique, a random sample is chosen from each stratum after the population has been divided into smaller groups or strata. This method is helpful when the population is diverse and you want to make sure that the sample is representative of many subgroups.

· Cluster Sampling:-This method involves segmenting the population into groups or clusters before randomly selecting a sample. This approach can be useful when a population is distributed over a large area. To poll everyone, though, would not be practical or possible.

· Systematic Sampling: This method requires picking a random beginning point before selecting every nth person in the population.

There Steps:-

· Choose your population of interest carefully: Consider carefully before selecting from the group of persons you believe should have their opinions collected. Include them in the sample after that.

· Determine a suitable sample frame: To get valid data, your frame should only include a sample from the demographic that interests you.

· Select your sample and start your survey: Finding the right sample and choosing an appropriate sample frame might be difficult at times. Even if all the odds are in your favour, unforeseen problems like cost, responder quality, and response time may still arise. It may be challenging, but it is feasible, to precisely predict the responses of a sample in a probability survey.

2. Non-probability:-It sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.

Non probability sample types:-

· Convenience sampling:- This strategy uses accessible subjects to finish a study, as the name suggests. This may be friends, people on the street, or students in a class at a university. Although convenience sampling is quick and simple, the results cannot be generalised to a larger population.

· Snowball sampling: A snowball sample works by recruiting some sample members who in turn recruit people they know to join a sample. This method works well for reaching very specific populations who are likely to know others who meet the selection criteria.

· Explain probability and non-probability sampling techniques used for analysis of food samples.- Quota Sample: In quota sampling, targets are established for the number of respondents needed from each segment after dividing the population into subgroups based on factors like age or location. The main distinction between quota sampling and stratified random sampling is that quota sampling does not employ a random sampling technique. For instance, a researcher could use a convenience sample with specific quotas to guarantee that there are equal numbers of men and women included, but this technique would still not give every member of the population a chance of being chosen and would not be a probability sample.