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Difference Between Population And Sample Pdf

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The study of statistics revolves around the study of data sets. This lesson describes two important types of data sets - populations and samples. Along the way, we'll introduce simple random sampling, the main method used in this tutorial to select samples.

Population vs Sample – All you need to know

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree.

In order to assess the degree of this bias, the informed reader of medical literature should have some understanding of the population from which the sample was drawn. The ultimate decision on whether the results of a particular study can be generalized to a larger population depends on this understanding.

The subsequent deliberations dwell on sampling strategies for different types of research and also a brief description of different sampling methods. Research workers in the early 19th century endeavored to survey entire populations. This feat was tedious, and the research work suffered accordingly. Current researchers work only with a small portion of the whole population a sample from which they draw inferences about the population from which the sample was drawn.

This inferential leap or generalization from samples to population, a feature of inductive or empirical research, can be full of pitfalls. In clinical medicine, it is not sufficient merely to describe a patient without assessing the underlying condition by a detailed history and clinical examination.

The signs and symptoms are then interpreted against the total background of the patient's history and clinical examination including mental state examination. Similarly, in inferential statistics, it is not enough to just describe the results in the sample. One has to critically appraise the real worth or representativeness of that particular sample.

The following discussion endeavors to explain the inputs required for making a correct inference from a sample to the target population. Any inferences from a sample refer only to the defined population from which the sample has been properly selected.

We may call this the target population. The findings of this study, therefore, apply only to Delhi High Court lawyers from which a representative sample was taken. Of course, this finding may nevertheless be interesting, but only as a pointer to further research. The data on lawyers in a particular city tell us nothing about lawyers in other cities or countries. In statistics, a population is an entire group about which some information is required to be ascertained.

A statistical population need not consist only of people. We can have population of heights, weights, BMIs, hemoglobin levels, events, outcomes, so long as the population is well defined with explicit inclusion and exclusion criteria.

In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation.

The population must be fully defined so that those to be included and excluded are clearly spelt out inclusion and exclusion criteria. For example, if we say that our study populations are all lawyers in Delhi, we should state whether those lawyers are included who have retired, are working part-time, or non-practicing, or those who have left the city but still registered at Delhi.

Use of the word population in epidemiological research does not correspond always with its demographic meaning of an entire group of people living within certain geographic or political boundaries.

A population for a research study may comprise groups of people defined in many different ways, for example, coal mine workers in Dhanbad, children exposed to German measles during intrauterine life, or pilgrims traveling to Kumbh Mela at Allahabad.

When generalizing from observations made on a sample to a larger population, certain issues will dictate judgment. For example, generalizing from observations made on the mental health status of a sample of lawyers in Delhi to the mental health status of all lawyers in Delhi is a formalized procedure, in so far as the errors sampling or random which this may hazard can, to some extent, be calculated in advance.

However, if we attempt to generalize further, for instance, about the mental statuses of all lawyers in the country as a whole, we hazard further pitfalls which cannot be specified in advance. We do not know to what extent the study sample and population of Delhi is typical of the larger population — that of the whole country — to which it belongs. The dilemmas in defining populations differ for descriptive and analytic studies. In descriptive studies, it is customary to define a study population and then make observations on a sample taken from it.

Study populations may be defined by geographic location, age, sex, with additional definitions of attributes and variables such as occupation, religion and ethnic group. In field studies, it may be desirable to use a population defined by an administrative boundary such as a district or a state.

This may facilitate the co-operation of the local administrative authorities and the study participants. However, administrative boundaries do not always consist of homogenous group of people. Since it is desirable that a modest descriptive study does not cover a number of different groups of people, with widely differing ways of life or customs, it may be necessary to restrict the study to a particular ethnic group, and thus ensure better genetic or cultural homogeneity.

Alternatively, a population may be defined in relation to a prominent geographic feature, such as a river, or mountain, which imposes a certain uniformity of ways of life, attitudes, and behavior upon the people who live in the vicinity. If cases of a disease are being ascertained through their attendance at a hospital outpatient department OPD , rather than by field surveys in the community, it will be necessary to define the population according to the so-called catchment area of the hospital OPD.

For administrative purposes, a dispensary, health center or hospital is usually considered to serve a population within a defined geographic area. But these catchment areas may only represent in a crude manner with the actual use of medical facilities by the local people.

For example, in OPD study of psychiatric illnesses in a particular hospital with a defined catchment area, many people with psychiatric illnesses may not visit the particular OPD and may seek treatment from traditional healers or religious leaders.

Catchment areas depend on the demography of the area and the accessibility of the health center or hospital. Accessibility has three dimensions — physical, economic and social. It depends on the topography of the area e. Economic accessibility is the paying capacity of the people for services.

Poverty may limit health seeking behavior if the person cannot afford the bus fare to the health center even if the health services may be free of charge. It may also involve absence from work which, for daily wage earners, is a major economic disincentive. Social factors such as caste, culture, language, etc. In such situations, the patient may feel more comfortable with traditional healers.

Ascertainment of a particular disease within a particular area may be incomplete either because some patient may seek treatment elsewhere or some patients do not seek treatment at all. Focus group discussions qualitative study with local people, especially those residing away from the health center, may give an indication whether serious underreporting is occurring.

When it is impossible to relate cases of a disease to a population, perhaps because the cases were ascertained through a hospital with an undefined catchment area, proportional morbidity rates may be used. These rates have been widely used in cancer epidemiology where the number of cases of one form of cancer is expressed as a proportion of the number of cases of all forms of cancer among patients attending the same hospital during the same period.

As opposed to descriptive studies where a study population is defined and then observations are made on a representative sample from it, in case control studies observations are made on a group of patients. This is known as the study group , which usually is not selected by sampling of a defined larger group. For instance, a study on patients of bipolar disorder may include every patient with this disorder attending the psychiatry OPD during the study period.

One should not forget, however, that in this situation also, there is a hypothetical population consisting of all patients with bipolar disorder in the universe which may be a certain region, a country or globally depending on the extent of the generalization intended from the findings of the study.

Case control studies are often carried out in hospital settings because this is more convenient and accessible group than cases in the community at large. However, the two groups of cases may differ in many respects. At the outset of the study, it should be deliberated whether these differences would affect the external validity generalization of the study.

Usually, analytic studies are not carried out in groups containing atypical cases of the disorder, unless there is a special indication to do so. Basically, cohort studies compare two groups of people cohorts and demonstrate whether or not there are more cases of the disease among the cohort exposed to the suspected cause than among the cohort not exposed.

To determine whether an association exists between positive family history of schizophrenia and subsequent schizophrenia in persons having such a history, two cohorts would be required: first, the exposed group, that is, people with a family history of mental disorders the suspected cause and second, the unexposed group, that is, people without a family history of mental disorders.

These two cohorts would need to be followed up for a number of years and cases of schizophrenia in either group would be recorded. If a positive family history is associated with development of schizophrenia, then more cases would occur in the first group than in the second group. The crucial challenges in a cohort study are that it should include participants exposed to a particular cause being investigated and that it should consist of persons who can be followed up for the period of time between exposure cause and development of the disorder.

It is vital that the follow-up of a cohort should be complete as far as possible. If more than a small proportion of persons in the cohort cannot be traced loss to follow-up or attrition , the findings will be biased , in case these persons differ significantly from those remaining in the study.

Depending on the type of exposure being studied, there may or may not be a range of choice of cohort populations exposed to it who may form a larger population from which one has to select a study sample. For instance, if one is exploring association between occupational hazard such as job stress in health care workers in intensive care units ICUs and subsequent development of drug addiction, one has to, by the very nature of the research question, select health care workers working in ICUs.

On the other hand, cause effect study for association between head injury and epilepsy offers a much wider range of possible cohorts. Difficulties in making repeated observations on cohorts depend on the length of time of the study. In correlating maternal factors pregnancy cohort with birth weight, the period of observation is limited to 9 months.

However, if in a study it is tried to find the association between maternal nutrition during pregnancy and subsequent school performance of the child, the study will extend to years. For such long duration investigations, it is wise to select study cohorts that are firstly, not likely to migrate, cooperative and likely to be so throughout the duration of the study, and most importantly, easily accessible to the investigator so that the expense and efforts are kept within reasonable limits.

Occupational groups such as the armed forces, railways, police, and industrial workers are ideal for cohort studies. Future developments facilitating record linkage such as the Unique Identification Number Scheme may give a boost to cohort studies in the wider community. A sample is any part of the fully defined population.

A syringe full of blood drawn from the vein of a patient is a sample of all the blood in the patient's circulation at the moment. Similarly, patients of schizophrenia in a clinical study is a sample of the population of schizophrenics, provided the sample is properly chosen and the inclusion and exclusion criteria are well defined. To make accurate inferences, the sample has to be representative. A representative sample is one in which each and every member of the population has an equal and mutually exclusive chance of being selected.

Inputs required for sample size calculation have been dealt from a clinical researcher's perspective avoiding the use of intimidating formulae and statistical jargon in an earlier issue of the journal.

A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. In medical research, the criteria for population may be clinical, demographic and time related. Clinical and demographic characteristics define the target population, the large set of people in the world to which the results of the study will be generalized e. The study population is the subset of the target population available for study e.

Snowball sample, where one case identifies others of his kind e. Non-random samples have certain limitations. The larger group target population is difficult to identify. This may not be a limitation when generalization of results is not intended. The results would be valid for the sample itself internal validity.

They can, nevertheless, provide important clues for further studies based on random samples. Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples. However, in some situations, the investigator has to make crucial judgments. One should remember that random samples are the means but representativeness is the goal.

Population vs sample: what’s the difference?

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. What is the difference between a population and a sample? What common variables and statistics are used for each one, and how do those relate to each other? The population is the set of entities under study.


In the same way, in statistics population denotes a large group consisting of elements having at least one common feature. The term is often.


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Sign in. P eople often fail to properly distinguish between population and sample. It is however essential in any statistical analysis, starting from descriptive statistics with different formulas for variance and standard deviation depending on whether we face a sample or a population. Moreover, the branch of statistics called inferential statistics is often defined as the science of drawing conclusions about a population from observations made on a representative sample of that population. It is therefore crucial to properly distinguish between the two concepts.

Home QuestionPro Products Audience. The concept of population vs sample is an important one, for every researcher to comprehend. Understanding the difference between a given population and a sample is easy. You must remember one fundamental law of statistics: A sample is always a smaller group subset within the population. In market research and statistics, every study has an essential inquiry at hand.

In the same way, in statistics population denotes a large group consisting of elements having at least one common feature. The term is often contrasted with the sample , which is nothing but a part of the population that is so selected to represent the entire group. Population represents the entirety of persons, units, objects and anything that is capable of being conceived, having certain properties.

Statistics without tears: Populations and samples

Many people fail to understand the basic difference between the population and the sample. However, when analyzing data it is vital to know the difference between the two terms. The difference between population and sample is that the population includes all the units from a set of data.

Skip to main content. Lead Author s : Dr. Source: Edmodo. Student Price: Contact us to learn more. In this homework assignment students will be asked to understand population, sample and various sampling techniques. This content is licensed under the Creative Commons Attribution 4. Comprehend the concept like, Population, Sample, sampling, sampling error etc.

Published on May 14, by Pritha Bhandari. Revised on February 15, A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc. Table of contents Collecting data from a population Collecting data from a sample Population parameter vs sample statistic Quiz: Populations vs samples Frequently asked questions about samples and populations.

Difference Between Population and Sample

Key Differences Between Population and Sample

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What is the difference between population and sample?

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5 Comments

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Jeanne A. 05.05.2021 at 07:29

What Is the Difference Between a. Sample and a Population, and Why. Are Samples Important? Samples are selected from populations. A population is the total of.

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Belle B. 11.05.2021 at 14:23

A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from.

Tiopaygraphho1973 12.05.2021 at 06:16

Research studies are usually carried out on sample of subjects rather than whole populations.

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