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How to Get a Taste of Everything: Sampling Techniques
Latest   Machine Learning

How to Get a Taste of Everything: Sampling Techniques

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July 17, 2023
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Last Updated on July 17, 2023 by Editorial Team

Author(s): Roli Trivedi

Originally published on Towards AI.

Mastering the art: Tips and tricks for successful sampling

Photo by Natalia Gusakova on Unsplash

Sampling is the process of selecting a subset(sample) from the population. It is done to get information about a population based on statistics from a subset of the population, i.e., a sample, without investigating every individual.

Types of Sampling

  • Probability Sampling: When we use probability to sample observation from the population. In probability sampling, each element has an equal chance to get selected.
  • Non Probability Sampling: When sampling is done on the basis of convenience and all the elements don't have an equal chance to get selected.
Source: Image by the author.

Probability Sampling Methods

  • Simple Random Sampling: One of the simplest probability sampling techniques. Here we select items at random. Every observation in the population has a chance of getting selected.
    For Example, if you have a bag of white and black balls and you want to select one ball from it, then if you pick a ball from the bag, there are equal chances of the white and black balls being picked.
Source: Image by the author.
  • Systematic Sampling: It is an extension of simple random sampling. Here we pick every n element from the first random element. The value of n is calculated by (total objects/sample size).
    For Example, you want to pick 6 random balls, and there are 20 total balls.
    n = 20/6 i.e 3
    So you will pick every 3rd ball from the first random ball.
    Note: It might also lead to bias if there is an underlying pattern in which we are selecting items from the population. Therefore make sure there is no hidden pattern.
Source: Image by the author.
  • Stratified Sampling: You will segregate the population based on the attribute which you want to study. Then from each subgroup randomly pick one element. Here each subgroup is called strata. We use this type of sampling when we want representation from all subgroups of the population.
    For example, suppose a researcher wants to conduct a study on the satisfaction level of employees in a large company. They might divide the employees into strata based on their departments and then randomly select participants from each department.
Source: Image by the author.
  • Cluster Sampling: This type of sampling is used when we focus on a region area. Here we use subgroups of the population as sampling units rather than individuals. The population is divided into clusters, and then a cluster is selected at random as a sampling unit.
    For example, suppose a researcher wants to study the eating habits of people in a certain city. They might divide the city into clusters based on the neighborhoods and then randomly select some of the neighborhoods to be included in the study. Within each selected neighborhood, the researcher could then select a sample of individuals to participate in the study.
Source: Image by the author.

Non-Probability Sampling Methods

  • Convenience Sampling: Here, individuals are selected based on their availability and willingness to take part. It is convenient for participants if they want to participate or not.
    For Example, NGOs conduct convenience sampling at malls by distributing leaflets about upcoming events. They do that by standing at the mall entrance and giving out randomly.
Source : Image by the author.
  • Quota Sampling: We choose items from a predetermined characteristic of the population. Here the population is divided into groups called strata, and then non-probability sampling, like convenience sampling or judgmental sampling, is applied to these groups based on quota. In quota sampling, the chosen sample might not be the best representation of the characteristics of the population that weren’t considered.
    For Example, Consider that we have to select individuals
    having a number in multiples of four for our sample. Therefore, the individuals numbered 4, 8, 12, 16, and 20 are already reserved for our sample.
Source : Image by the author.
  • Snowball Sampling: Here, existing people are asked to nominate further people known to them so that the sample increases in size like a rolling snowball. This is effective when the sampling frame is difficult to identify. However, there is a significant risk of selection bias as referenced people will share common traits with the person who recommends them. This method of sampling is effective when a sampling frame is difficult to identify.
    For example, there is research going on a particular product, so a number of users are selected for it, and then those users are asked to pick the next set of people who could try the product. This will be called snowball sampling. Here, we had randomly chosen person 1 for our sample, and then he/she recommended person 6, person 6 recommended person 11, and so on.
    1 > 6 > 11 > 14 > 19
Source: Image by the author.
  • Judgmental Sampling: It is also called selective sampling. It depends on the judgment of experts when choosing who should participate. It is also prone to bias by experts.
    For Example, auditors find a basket more fishy as compared to other ones, so they select that particular basket for auditing. Here each basket doesn’t have an equal probability of being selected. Assume that our experts agree that people with the numbers 1, 7, 10, 15, and 19 should be included in our sample since they can help us better infer the population.
Source: Image by the author.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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