Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Population in statistics means the whole of the information which comes under the preview of statistical investigation.
In other words, an aggregate of objects animate or in animate under study is the population.
It is also known as “Universe”.
Sampling is necessary for the researchers and nursing students....
This PPT is basically related to 4th year nursing students....
It include sampling, sample, type of population, type of sampling technique and sampling error...
Sampling is a process of selecting sample...
Sample is a representative unit of the population...
Sampling is procedure or process of selecting some units from the population with some common characteristics and is primarily concerned with the collection of data of some selected units of the population.
This slideshow explains the details about Photosynthesis process. It has covered all the aspects such as definition, significance, photosystems, Hill reaction, Calvin cycle, HSK cycle, CAM pathway, Photorespiration, etc. of photosynthesis. This slide show will be useful to College students and the students who are appearing for various competitive examinations. .This slide show is equally beneficial to the students who want to pursue career in the biological sciences.
This slide is about academic and administrative audit for the quality control in the educational institutes. it also deals with various management techniques including Kaizen, 5S, etc. This slideshow is useful for the NAAC purpose.
This slideshow explains the complete process of writing research proposal for funding agencies. It is useful for the PhD students, researchers, R& D department of company personnel.
This slideshow is related to testing of hypothesis and goodness of fit of statistics. This may be useful for students, teachers, managers concerned with bio statistics, bioinformatics, data science, etc.
This slide show is related to measures of dispersion or variability in Statistics. This slideshow will be useful to all the students and persons interested in Statistics, Bio statistics, Management, Education, Data Science, etc.
This slideshow explains the important measures of central tendency in statistics. It deals with Mean, mode and median; its characteristics, its computation, merits and demerits. This slideshow will be useful to students, teachers and managers.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
This slide explains term biostatistics, important terms used in the field of bio statistics and important applications of biostatistics in the field of agriculture, physiology, ecology, genetics, molecular biology, taxonomy, etc.
This slide show is about overview of building blocks of life i.e. amino acids. It is describes physical, chemical properties, classification, biological functions, modified products of amino acids and biosynthesis of amino acids.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The ASGCT Annual Meeting was packed with exciting progress in the field advan...
Sample and sampling
1. Sample and Sampling
• Definition: It is the process of selection of a part of a
population from the population to represent the whole
population.
• The main objective of sampling is to get information
regarding the population from which the sample is obtained.
• The sampling enables to draw inferences about the whole
population.
1
2. Important terms related to Sampling
• Sampling unit: Every item or every individual in a population is referred as
sampling unit.
• Sample: It is the part selected from the population or a group of individuals
selected from population. It is enables to learn about the whole population by
observing a few individuals.
• Population: It is the totality or aggregate of individuals with specified
characteristics. Population may be finite or infinite.
• E.g. Finite population: Number of plants in a quadrat is finite, number of
students in the class.
• Infinite population: Number of phytoplanktons in a pond, number of stars in
the sky, number of viruses in human body.
2
3. Size of sample
1. It denotes the number of sampling units, that are selected from
population. Sample size is based on decided on the time, resources
available, reliability, and expected outcomes.
2. Before deciding the size of sample following aspects are to be
considered:
i) Larger the population size, bigger should be the sample size.
ii) If population has homogenous units, small size sample serves the
purpose however, if the population has heterogeneous units, the
large size sample is essential. 3
4. Size of sample
iii) The nature and purpose of study is also important in
determination of the size of sample. Small sample is suitable for
continuous and intensive study.
iv) The factors like availability of finance, time and trained
persons is important in sample size.
v) For random sampling, the large number of samples gives better
accuracy in results.
vi) As a rule of thumb 1/10 part of the whole population is
taken as sample size.
4
5. Choice of sampling methods
• It is difficult to say that a particular sampling method would always be
better than the other ones. Each method has its own merits and demerits.
• The choice of selection of a particular method depends on the number of
factors like the nature of the problem, the size of the sample, the size of
population, availability of finance, time and trained persons etc.
• If the size of sample is small in relation to the size of population, then
judgment sampling would yield better results. For large- size sample,
random sampling would be more appropriate.
• When sample units are heterogeneous, the stratified sampling may give
better results than simple random sampling.
5
6. Advantages of sampling
1.The study of whole population requires much time, physical labour
and finance. By sampling, one can reduce this, as only a few
selected items are studied.
2.In the studies where individuals are short-lived, the sampling is the
only appropriate method.
3.Sampling is the most appropriate for the study of infinite population.
4.It is easy to handle a sample unit than to handle a whole population
that consists of many units.
6
7. Limitations of sampling technique
1.If sampling is not done properly, then results may be false, inaccurate
and misleading.
2.Personal bias regarding the choice of sampling size and sampling
method leads to wrongs conclusions.
3.Although there are some shortcomings in sampling techniques, yet it
is a very useful method for biostatistical investigations.
7
8. Types of sampling methods
• The purpose of sampling and the nature of the population will determine
choice of the sampling type. However, selection of sample should be done in
such a way that the sample taken should be a true representative of population.
• Sampling methods must have less sampling error i.e. the sample must have
characteristics as close as possible to the value that the researcher might have
obtained, if he had studied the whole population.
• There are two main types of sampling methods:
1. Random Sampling (probability sampling)
2. Non-Random Sampling (non-probability)
8
9. Random sampling with replicates
• Replication is the repetition of equivalent measurements.
• Replication is an essential element of a good field design.
• Generally replicated measurements will be more representative if they are
independent of each other and interspersed across the community.
• Replication is the repetition of an experimental condition to estimate the variability
associated with the phenomenon.
Need of random sampling:
• It provides equal opportunity for an item to get selected from the population.
• If data is not randomized, then the data collected from this design is likely to provide
an inaccurate representation of the entire study area.
• However, random sampling without replicates saves time and money in sampling but
it is not the true representative.
9
10. Methodology of achieving randomness
• Consider some alternatives. A bad approach is to throw randomization out the
window and subjectively select your sampling locations to ensure interspersion.
• Another bad approach is to make your random selection, but discard it if it has poor
interspersion.
• The problem here is that subjective bias can creep into the process when you decide
whether to discard a scheme.
• A good version of this approach would be to decide ahead of time on an objective
way to cull out sample arrangements with unacceptably poor interspersion.
• In the example above, a good rule might be to accept only those arrangements with
quadrats in all four quarters of the study area.
10
11. Randomness
• Measurements are usually subject to variation and
uncertainty.
• The experiments are replicated to identify the
sources of variation, to estimate the true effects of
treatments, to strengthen the reliability and validity
of experiment and to add to the existing knowledge
of the topic.
11
13. Random sampling
• This is also called probability sampling method.
• In this method all the items in the population have an equal chance
of being selected.
• Random sample is not a haphazard choice but is a careful selection
to ensure that every item has an equal chance of inclusion.
• Random sampling is widely used in medical, agricultural and
biological sciences.
• Major types of random sampling are lottery method, random
number method, systematic sampling, stratified sampling, etc.
13
14. Simple Random sampling
• It is the type of random sampling.
• This is the most common method in which a random sample
is chosen in such a way that all the items have an equal
chance of appearing in the sample.
• It ensures the randomness
• There are two major methods of simple random sampling
• i) Lottery method or
• Ii) Random number method
14
15. Lottery method
• This is the most popular and the simplest method.
• In this method all the items of the population are numbered
on separate paper slips of identical size, shape and colour.
• These paper slips are folded and mixed in a box and blind
selection is made.
• In this, the selection of each item depends on chance.
• This is also called unrestricted random sampling because
samples are selected without any restriction. 15
16. Lottery method
Merits:
• It is very simple to perform and easy to understand.
• This is the most common method is biological and
agricultural sciences.
Demerits:
• The limitation of this method is that it is used only
for finite populations. It not suitable for infinite
population.
16
17. Random number method
• It is a popular and the most practical method of random
sampling.
• In this Table of random numbers are used in place of
paper slips and blind selection.
• Random number table (5 digit) of Snedecor and
Cochran (1988) are used either horizontally or vertically
for selection of sample and it is without bias.
• There are several random number tables viz. Tippets
table, Fisher and Yates table, Rao and Mitra table and
Snedecor and Cochran table.
17
18. Random number method
• One can use the table of random numbers
from any position either horizontally or
vertically e.g. if we want to select 10 pods
from 200 pods, then each pod is assigned
a number from 00001 to 00200.
• One can start at any line and column from
the table. The numbers, which fall in that
line and column are taken and accordingly
10 samples are selected.
18
Part of random number table
19. Simple random sampling
• Merits of simple random sampling:
1. It is a more scientific method because there are less chances of personal bias.
2. One can measure sampling error.
3. The theory of probability is applicable, as sample is random.
• Demerits of simple random sampling:
1. It requires a complete list of all the items of the population. Many a times an
update lists are not available.
2. This method is not useful when the units of population are spread over a large
area.
19
20. Systematic sampling
• Selection of random samples is very tedious when samples to be selected are
very large population.
• Systematic sampling method is practiced when population is large, scattered
and not homogenous.
• In this method the items are arranged in numerical or geographical or
alphabetical or any other order.
• Eg. Samples of trees from a forest or houses in a city. In such cases a systematic
sampling is applied. Population
20
21. Systematic sampling
• Systematic procedure follows to choose a sample by taking every Kth
individual, where K refers the sample interval calculated by the
formula:
• K= Total population/Sample size desired.
• Ex. 20% sample to be taken from 1000 individual of a population,
• K= 1,000/20% of 1,000= 5
• So the first sample will be 5th individual
• Second sample will be 10th individual and third sample will be 15th
individual.
21
22. Systematic sampling
Merits of systematic sampling:
1. This method is simple and convenient.
2. It is inexpensive as it saves time and labour.
3. To maintain the randomness and to minimize tedious selection, systematic sampling
is used.
4. The sample is evenly distributed over the whole population and hence all
contiguous parts of the population are represented in the sample.
Demerits:
1. The major demerit of this is that it may not represent the whole population.
2. There is no single reliable formula available for estimating the standard error of
sample.
22
23. Stratified sampling
• This method gives better results as compared to other methods when
population is heterogeneous with respect to variable under study.
• In this method of sampling, the population is divided into relatively
homogenous groups, called strata or sub-populations.
• A random sample is drawn out from each stratum to produce an
overall sample.
23
24. Stratified sampling
• Drawing out of sample is proportional or non-proportional.
• In the former, items are taken from each stratum in the
proportion of the units of the stratum to the total population.
• In non-proportional sampling, equal numbers of units are taken
from each stratum irrespective of its size.
• E.g. Agronomists may stratify a plot of land based on its known
fertility level and then take a sample of plants from each
different stratum to measure their yield.
24
25. Stratified sampling
Merits:
1. It is more representative as every group is represented in a sample.
2. This method is more appropriate when the original population is badly skewed.
3. In a non-homogeneous population, this method gives more reliable results.
Demerits:
1. There is always a problem of deciding the criterion for stratification.
2. Prior knowledge of the population is required for better stratification, but this is not
always possible.
3. Many a times the points of demarcation of the strata are not clear-cut and the strata
overlaps.
4. If proper stratification is not done, then the sampling will be biased.
25
26. Non-random sampling
• The samples selected by these methods do not permit all the items in
the population to have an equal chance of being selected.
• Non-random (non- probability) sampling method is rarely used
because the sample estimates are subject to greater variability than the
probability sampling.
• The most common types of non-random sampling techniques are
• Judgment sampling,
• Quota sampling and
• Convenience sampling
26
27. Judgment sampling
• In this method the choice of the sample items depends exclusively on the
judgment of the investigator.
• The investigator selects only those items of the population in the sample
which he thinks are the representative of the whole population.
• In this, the method of selection is based on predetermined criteria. e.g. if a
sample of 10 plants of wheat bearing reproductive tillers is to be taken
from a plot of 100 plants for analyzing the yield of the plants, the
experimenter would select only 10 plants with a greater number of tillers
which he thinks are the representative of the whole population.
•
27
28. Judgment sampling
Merits:
1.It is a simple method.
2.It is useful when the size of the sample of the population is small.
3.It is very useful when sampling needed to be done under time
constraint.
Demerits:
1.The sample may not be a representative one due to individual bias.
2.The estimates are not accurate.
3.The results obtained can not be compared with other sampling
studies. 28
29. Convenience sampling
• As the name implies this technique is simply convenient to the
researcher in terms of time, money and administration.
• It also known as Chunk sampling.
• This method is occasionally used in special circumstances.
• Generally this method is not used in making inferences of the whole
population.
• This method is usually used for pilot studies before a final sampling
plan is decided upon.
• For example you can pick out 100 people to be surveyed simply from
telephone directory.
29
30. Convenience sampling
Merits:
1.It is a convenient method for researcher in terms of money,
time and administration.
2.The selection of sample is easy.
Demerits:
1.This method is biased.
2.The results obtained are unsatisfactory as they can not be
representative of the whole population.
30
31. Quota sampling
• This is most used in non-random categories.
• In this method sample quotas are fixed for characteristic of population.
• The selection of sample item in each quota depends on personal judgment.
• This method is a combination of judgment sampling and convenient
sampling.
• E.g. an animal scientist recognizing that variability in the daily milk
production is due to the age differences in cows. So he will fix quota for
cows from the different age groups. For instance 30% of cows between 4-6
years and remaining 70% of cows between 6-8 years old.
31
32. Quota sampling
Merit:
• It requires less money and time.
Demerits:
1.It is based on personal bias.
2.The samples may not be representative of the whole
population.
32