SAMPLING

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sampling

PRESENTATION

5.-

Zavala García Génesis

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Hernández Herrera Paloma

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Ricardo Alvarado Alexander

Gascón Rodríguez Alexa Rubí

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team members:

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Chávez González Melinda

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Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population. Sampling allows researchers to conduct studies about a large group by using a small portion of the population. Sampling means selecting the group that you will actually collect data from in your research.

WHAT IS SAMPLING?

Who created the sampling?

Where was the sampling created?

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When was the sampling created?

TYPES OF SAMPLING

NON PROBABILISTIC SAMPLING

Non-probabilistic sampling is a sampling technique where samples are collected through a process that does not give all individuals in the population the same opportunities to be selected.

PROBABILISTIC SAMPLING

Probabilistic sampling is a technique in which samples are collected through a process that gives all individuals in the population the same opportunity to be selected.

NON PROBABILISTIC SAMPLING

Convenience sampling: convenience sampling is based on available subjects, such as stopping people on the street corner as they pass by. It is useful if the researcher wants to study the characteristics of people who pass the corner of a street at a given time.Deliberate, critical or judgmental: is one that is selected based on the knowledge of a population or purpose of the study. Snowball sampling: t is appropriate to use a snowball sampling when members of a population are difficult to locate, such as homeless people, migrant workers or undocumented immigrants. It is one in which the researcher collects data about the few members of the target population that he can locate, and then asks them to provide him with the necessary information to locate other members who know about that population.Sampling by quotas: If you are a researcher who is taking a national quota sample, you may need to know what proportion of the population is male and what proportion is female.

PROBABILISTIC SAMPLING

Simple random sampling: is the basic sampling method used in statistical methods and calculations. To collect a simple random sample, each unit of the target population is assigned a number. Then a set of random numbers is generated and the units that have those numbers are included in the sample.Systematic sampling: is where elements of the population are put into a list and then every umpteenth element in the list is systematically selected for inclusion in the sample.Stratified sampling: is a sampling technique in which the researcher divides the entire target population into different subgroups or strata, and then randomly selects the final subjects from the different strata proportionally.Cluster sampling: it can be used when it is impossible or impractical to develop an exhaustive list of the elements that constitute the target population. However, generally the elements of the population are already grouped into subpopulations and lists of those subpopulations already exist or can be created.

ADVANTAGES OF SAMPLING

Improved decision-making: Sampling results can inform informed decisions.

Error reduction: A well-planned sampling can minimize errors and biases.

Possibility of updating information: It allows for updating information about the population on a regular basis.

Detailed analysis: A more detailed and in-depth analysis of the data collected can be performed.

ADVANTAGES OF SAMPLING

Flexibility: There are various sampling methods (random, stratified, systematic, etc.) that can be adapted to different situations.

Precision: A well-designed sample can provide accurate estimates about the total population.

Representativeness: A representative sample allows results to be generalized to the entire population.

Cost and time efficiency: Conducting a study on a sample is faster and cheaper than studying the entire population.

Mention three advantages of sampling:

Who created the sampling?

Mention two types of probabilistic sampling:

When and where was the sampling created?

What is the non-probabilistic sampling?

How should the sampling be?

What is sampling?

Types of sampling:

QUESTIONS

• Analysis
• Data
• exploration
• interval
• likelihood
• metodology
• random
• subsamples
• assessment
• estimation
• mean
• population
• sample
• variance

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THANKS!

Sampling does not have a single inventor. However, key figures in the development of sampling methods include Sir Ronald A. Fisher and Jerzy Neyman. Fisher made fundamental contributions to statistical theory and experimental design, while Neyman and his collaborator Egon Pearson developed methods for sampling theory and estimation.

Sampling as a formal technique in statistics began to be developed in the 20th century, although the fundamental concepts already existed in older methods of research and data collection. However, systematic sampling and its formal methods began to take shape in the mid-20th century with the advancement in statistical theory.

The development of modern sampling took place mainly in the United States and Europe. Pioneering work in the area was done in these places where statistics and social research were advancing rapidly.

HOW IT WORKS

It can be difficult for researchers to conduct accurate studies on large populations. In some cases, it can be impossible to study every individual in the group. That's why they often choose a small portion to represent the entire group. This is called a sample. Samples allow researchers to use characteristics of the small group to make estimates of the larger population.The chosen sample should be a fair representation of the entire population. When taking a sample from a larger population, it is important to consider how the sample is chosen. To get a representative sample, it must be drawn randomly and encompass the whole population.