What is the difference between a sample and a population in academic research?

As I am instructing a "Population Genetics" course for graduate students, I believe this question is excessively simple! In academic research, a population refers to the entire group that the study aims to investigate, while a sample is a subset of that population chosen for practical reasons. The sample is analyzed to make inferences about the larger population. Proper sampling methods ensure the sample is representative, allowing researchers to generalize findings back to the broader population with statistical confidence. Understanding this distinction is crucial for drawing accurate and applicable conclusions in research studies!

In academic research, a population refers to the entire group that the study aims to generalize findings to, while a sample is a subset of that population selected for the actual study.

In statistics, a population is the whole group you want to study / make conclusions about in your research. Depending on the study, this can be all people on Earth, all registered voters in a country/county, or all animals in a given species or geographic area. Because surveying all members of a population is usually prohibitively expensive/impractical/time-consuming, we resort to sampling them.

In research, the population is everyone you're interested in studying. For example, if you want to know about all the teenagers in a city, that's your population. But it's often too big to study everyone, so we use a sample, which is a smaller group from the population. This sample represents the larger group. For instance, if you pick 200 teenagers from that city to study, they're your sample. We study the sample to make conclusions about the whole population. There are specific formulas to calculate that sample size. The appropriate sample size is a crucial consideration in research design, as it impacts the accuracy and reliability of the study’s findings.

When conducting academic research, a population is the count of every case or person that meets the eligibility requirements of your study.

I agree with that definition, a sample is a subset of the population that is selected for study. It is often impractical or impossible to study the entire population, so researchers select a smaller group, the sample, to represent the population. The goal of sampling is to obtain a sample that is representative of the population, meaning that it accurately reflects the characteristics of the population. Using the example of studying the average height of adults, you might select a sample of 1000 adults from various regions and demographics within the country to represent the entire adult population.

When conducting academic research, a sample is a selection of cases or people that meet the eligibility requirements of your study.

A sample is a subset of the population that we study, survey, and get data from. This subset should be representative of the entire population--sharing the same characteristics in the same proportions. By studying the sample, we try to draw inferences/conclusions about the entire population. The accuracy of these depends heavily on the representativeness (or 'quality') of the sample.

En recherche académique, la population désigne l'ensemble complet des sujets d'intérêt, tandis qu'un échantillon est un groupe restreint de cette population, choisi pour être étudié. L'échantillon est crucial car il doit représenter fidèlement la population pour que les résultats de l'étude soient valides et généralisables. Le choix judicieux d'un échantillon, à travers des méthodes de sélection rigoureuses, est donc essentiel pour l'intégrité et la pertinence des conclusions de la recherche.

Uma amostra é uma parte representativa da popula??o que é selecionada para participar de um estudo. é impraticável e muitas vezes impossível examinar cada membro da popula??o, ent?o a amostra é usada para fazer inferências sobre a popula??o maior. No exemplo anterior, a amostra seria um grupo selecionado aleatoriamente de estudantes universitários da institui??o em quest?o.

Pursuant to my earlier explanations, samples should be randomly chosen and should ideally be strongly representative of the population we want to study. Specifically, they should represent the population's characteristics in the same proportions, such that the conclusions we draw from the sample are also valid for the entire population. Because of the impracticality of sampling/surveying the entire population, we must ensure to choose an adequate sample size, and to take multiple samples as need be to ensure that the sample is highly representative of the population in the characteristic we want to study.

As amostras permitem que os pesquisadores extrapolem conclus?es sobre uma popula??o maior a partir de um conjunto menor de dados. Isso torna a pesquisa mais viável e acessível. Uma amostra representativa é essencial para garantir que as conclus?es do estudo sejam generalizáveis para a popula??o de interesse.

Research samples and populations are necessary because they influence the trustworthiness and consistency of study outcomes. Selecting a representative sample is essential to have strong reliability and validity.

Selecting a proper sample size is important for reliable and accurate results for your research. Having a small sample of a large population could lead to skewed or inaccurate results.

Dans la recherche académique, la population englobe l'intégralité des cas pertinents pour une question de recherche donnée, tandis qu'un échantillon est une portion de cette population, soigneusement sélectionnée pour l'étude. La distinction est vitale car elle permet aux chercheurs d'obtenir des insights significatifs sur une population entière à partir de l'analyse d'un échantillon représentatif. Les échantillons et les populations sont au c?ur de la recherche, car ils déterminent la validité, la fiabilité et la généralisabilité des conclusions tirées, influen?ant ainsi directement la contribution de l'étude à la connaissance existante.

Choosing a sample size involves balancing statistical considerations with practical constraints. Factors to consider include the desired level of confidence, the margin of error acceptable, and the variability within the population. Generally, larger sample sizes provide more precise estimates but may be impractical due to time and resource limitations. Utilizing statistical formulas or online calculators can help determine an appropriate sample size based on these factors and the research design. Additionally, considering the study's objectives and the feasibility of recruitment and data collection can inform decisions about sample size.

Choosing a sample size in research involves considering factors like the desired level of precision, confidence level, variability in the population, and available resources. Generally, larger sample sizes provide more reliable results, but practical constraints often influence the decision. Researchers may use statistical formulas or software to determine an appropriate sample size based on the study's objectives and parameters.

It's like thinking of population as a big cake, and the sample as a slice of that cake representing the whole, that for some reason makes it peculiar. the sample can provides insights and conclusions that can be generalized to the larger population if you choose it properly, to make inferences about the population in a practical or feasible way in terms of time and resources.

We have to consider statistical principles like the targeted level of precision, the confidence level we want, the amount of time/resources/money we have to devote to our study, and what the characteristics studied are. Ideally, we'd want to use random sampling, and take multiple samples. If we come to similar inferences/conclusions about the population from multiple valid samples, then our confidence is greater that the samples represent the population, assuming no errors in methodology.

Dans la recherche académique, différencier échantillon et population est fondamental. La population est l'ensemble total d'individus concernés par la recherche, tandis que l'échantillon est un sous-ensemble sélectionné de cette population pour l'analyse. Choisir une taille d'échantillon adéquate est crucial pour équilibrer précision et faisabilité : trop petit, l'échantillon peut manquer de puissance statistique ; trop grand, il peut devenir inefficace en termes de co?ts et de temps. Des méthodes statistiques et considérations pratiques guident ce choix, visant à optimiser la représentativité et la fiabilité des résultats.

In addition to convenience, non-probability sampling also is necessary when trying to get participants from anonymous groups or groups whose characteristics would make it impossible to sample by more traditional methods. Examples of these could be members of anonymous groups, people who participate in illegal activity or activity that is kept secretive. Sampling must be decided to support the study, and there are no designs that are universally better than others.

Dans la recherche académique, la population représente l'ensemble complet des cas d'intérêt, tandis que l'échantillon est un sous-ensemble sélectionné de cette population pour l'étude. Le choix d'une technique d'échantillonnage est déterminant pour la qualité de la recherche : il doit garantir que l'échantillon soit représentatif de la population, permettant ainsi la généralisation des résultats. Les techniques varient de l'échantillonnage aléatoire, qui assure une sélection impartiale, à des méthodes plus ciblées comme l'échantillonnage stratifié, qui cherche à capturer la diversité au sein de la population. Le choix dépend des objectifs spécifiques de l'étude, des caractéristiques de la population et des contraintes pratiques.

I would choose sampling techniques based on the research question (what are we trying to determine?), the population's known characteristics (rough size, heterogeneity), practical considerations (budget, time), and ethical limitations. Depending on the above, I might suggest a simple random sample, or clustered/stratified samples.

A escolha da técnica de amostragem depende da natureza da pesquisa, do acesso aos membros da popula??o e dos recursos disponíveis. Técnicas comuns incluem amostragem aleatória simples, amostragem estratificada, amostragem por conglomerados, entre outras.

This is super critical for showing the applicability and scope of your research. As a data scientist who teaches research statistics and concepts in data quality, the description of the sample is paramount because it allows us to talk about whether the conclusions of the study or the data associated with the study are applicable to another study. As data mining and data sharing becomes more and more prevalent, we have to pay increased attention to this crucial concept.

Ao descrever a metodologia da pesquisa, é importante fornecer informa??es detalhadas sobre a popula??o de estudo, incluindo critérios de inclus?o e exclus?o, bem como detalhes sobre como a amostra foi selecionada. Isso permite que outros pesquisadores avaliem a validade e a generalizabilidade dos resultados.

Em pesquisa acadêmica, a popula??o refere-se ao grupo total de elementos ou casos que têm características específicas que est?o sendo estudadas. A amostra, por outro lado, é um subconjunto representativo dessa popula??o, escolhido para ser investigado devido à sua viabilidade prática. Em resumo, a popula??o é o conjunto completo de unidades de estudo, enquanto a amostra é uma sele??o menor e representativa dessa popula??o, usada para fazer inferências sobre o todo. Isso é frequentemente aplicado quando estudar a popula??o inteira seria impraticável devido a limita??es de tempo, recursos ou logística.

A sample is a subset of the population that is selected for data collection. A population is the entire group of interest that the researcher wants to study. For example, if you want to know the average height of students in a school, the population is all the students in the school, and the sample is a group of students that you measure. The sample should be representative of the population, meaning that it reflects the characteristics and diversity of the population as closely as possible. Sampling is a technique that allows researchers to make inferences about the population based on the sample data.

A qualidade da amostragem é crucial para a validade externa e a generaliza??o dos resultados. Ao relatar os resultados, é importante reconhecer quaisquer limita??es da amostra e discutir como essas limita??es podem afetar a interpreta??o dos resultados.