Multistage Sampling Method

<p>In two-stage cluster sampling, a simple random sample of clusters is selected and then a simple random sample is selected from the units in each sampled cluster.</p><p>Multi stage sampling is a generalisation of two stage sampling. As the name suggests, multi stage sampling is carried out in different stages. In each stage progressively smaller (population) geographic areas will be randomly selected. </p><p><b>Example:</b></p><p>A political pollster interested in assembly elections in Andhra Pradesh may first divide the state into different assembly units and a sample of assembly constituencies may be selected in the first stage. <br /><br />In the second stage, each of the sampled assembly constituents are divided into a number of segments and a second stage sampled assembly segments may be selected. <br /><br />In the third stage within each sampled assembly segment either all the house-holds or a sample random of households would be interviewed. <br /><br />In this sampling method, it is possible to take as many stages as are necessary to achieve a representative<br />sample. Each stage results in a reduction of sample size.</p> In a multi stage sampling at each stage of sampling a suitable method of sampling is used. More number of stages are used to arrive at a sample of desired sampling units.<br /><br /><b>Advantages</b><br /><br />a) Multistage sampling provides cost gains by reducing the data collection on costs.<br /> <br />b) Multistage sampling is more flexible and allows us to use different sampling procedures in different stages of sampling.<br /><br />c) If the population is spread over a very wide geographical area, multistage sampling is the only sampling method available in a number of practical situations.<br /><br /><b>Limitations</b><br /><br />a) If the sampling units selected at different stages are not representative multistage sampling becomes less precise and efficient.

Research Methodology Multistage Sampling Method

In two-stage cluster sampling, a simple random sample of clusters is selected and then a simple random sample is selected from the units in ...

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How systematic sampling is differed from Stratified Random Sampling

<p>Systematic sampling and stratified random sampling are both probability sampling techniques used to select a representative sample from a population, but they go about it in different ways:</p><div _ngcontent-ng-c2732142626="" class="container"></div><div _ngcontent-ng-c2732142626="" class="container"></div><p data-sourcepos="3:1-3:31"><strong>Stratified Random Sampling:</strong></p><ul data-sourcepos="5:1-7:0"><li data-sourcepos="5:1-5:327"><strong>Divides the population:</strong> Here, you first divide the entire population (let's say, all students in a school) into subgroups (strata) based on shared characteristics (like grade level). These subgroups should be mutually exclusive and collectively exhaustive (every member of the population belongs to exactly one subgroup).</li><li data-sourcepos="6:1-7:0"><strong>Random selection within subgroups:</strong> Then, you randomly select a sample from <strong>each subgroup</strong>. This ensures all relevant subgroups are represented in the final sample.</li></ul><p data-sourcepos="8:1-8:24"><strong>Systematic Sampling:</strong></p><ul data-sourcepos="10:1-12:0"><li data-sourcepos="10:1-10:145"><strong>Ordering the population:</strong> This method treats the population as a single list in some order (like a list of students in alphabetical order).</li><li data-sourcepos="11:1-12:0"><strong>Fixed interval selection:</strong> You decide on a sampling interval by dividing the total population size by your desired sample size. Then, you pick a random starting point from the list and select every nth element thereafter based on the interval.</li></ul><p data-sourcepos="13:1-13:47">Here's a table summarizing the key differences:</p><div class="horizontal-scroll-wrapper"> <div class="table-block-component"><div _ngcontent-ng-c1375581934="" class="table-block"><div _ngcontent-ng-c1375581934="" class="table-content"><table data-sourcepos="15:1-19:143"><tbody><tr data-sourcepos="15:1-15:78"><th data-sourcepos="15:1-15:25">Feature</th><th data-sourcepos="15:27-15:54">Stratified Random Sampling</th><th data-sourcepos="15:56-15:76">Systematic Sampling</th></tr><tr data-sourcepos="17:1-17:63"><td data-sourcepos="17:1-17:25">Divides population</td><td data-sourcepos="17:27-17:56">Yes, into subgroups (strata)</td><td data-sourcepos="17:58-17:61">No</td></tr><tr data-sourcepos="18:1-18:108"><td data-sourcepos="18:1-18:25">Basis for selection</td><td data-sourcepos="18:27-18:61">Random selection within subgroups</td><td data-sourcepos="18:63-18:106">Fixed interval selection from ordered list</td></tr><tr data-sourcepos="19:1-19:143"><td data-sourcepos="19:1-19:25">Risk of bias</td><td data-sourcepos="19:27-19:72">Lower, ensures all subgroups are represented</td><td data-sourcepos="19:74-19:141">Higher if the ordering coincides with a pattern in the population</td></tr></tbody></table></div><div _ngcontent-ng-c1375581934="" class="table-footer hide-from-message-actions ng-star-inserted"></div></div></div></div><div class="horizontal-scroll-wrapper"><div class="table-block-component"><div _ngcontent-ng-c1375581934="" class="table-block"></div></div> </div><p data-sourcepos="21:1-21:30"><strong>Choosing the right method:</strong></p><ul data-sourcepos="23:1-25:0"><li data-sourcepos="23:1-23:132">Use stratified random sampling if you have a diverse population with subgroups you want to be sure are represented in the sample.</li><li data-sourcepos="24:1-25:0">Use systematic sampling if ordering the population is easy and there's no underlying pattern or cyclical trend within the population that might bias your selection. It can also be slightly more efficient to implement than stratified sampling.</li></ul><p data-sourcepos="26:1-26:12"><strong>Example:</strong></p><p data-sourcepos="28:1-28:72">Imagine you want to survey students about their preferred lunch options.</p><ul data-sourcepos="30:1-31:372"><li data-sourcepos="30:1-30:198"><strong>Stratified Random Sampling:</strong> You could divide the students into subgroups by grade level (strata). Then, randomly select a sample from each grade level to ensure all grade levels have a voice.</li><li data-sourcepos="31:1-31:372"><strong>Systematic Sampling:</strong> If the student list is in alphabetical order (not ideal, but possible), you could choose a random starting student and then survey every 10th student on the list (assuming you want a 10% sample). However, this could be biased if, for example, taller students tend to be placed alphabetically later (and perhaps have different lunch preferences).</li></ul>

Research Methodology How systematic sampling is differed from Stratified Random Sampling

Systematic sampling and stratified random sampling are both probability sampling techniques used to select a representative sample from a po...

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Stratified sampling method - proportional stratified sample and disproportional stratified sample

<p>The stratified sampling method is used when the population is heterogeneous rather than homogeneous. <br /><br />A heterogeneous population is composed of unlike elements such as male/female, rural/urban, literate/illiterate, high income/low income groups, etc. <br /><br />In such cases, use of simple random sampling may not always provide a representative sample of the<br />population. <br /><br />In stratified sampling, we divide the population into relatively homogeneous groups called strata. Then we select a sample using simple random sampling from each stratum. <br /><br />There are two approaches to decide the sample size from each stratum, namely, proportional stratified sample and disproportional stratified sample. With either approach, the stratified sampling guarantees that every unit in the population has a chance of being selected. </p><p><b>We will now discuss these two approaches of selecting samples.</b></p><p data-sourcepos="1:1-1:282">Imagine you're a researcher studying political opinions, and you want to ensure your findings represent the whole population. But the population itself is diverse - there might be young voters, retirees, and working professionals. A simple random sample might miss a group entirely!</p><div _ngcontent-ng-c2732142626="" class="container"></div><div _ngcontent-ng-c2732142626="" class="container"></div><p data-sourcepos="3:1-3:201">This is where stratified sampling comes in. You divide the population (voters) into subgroups (strata) based on relevant characteristics (age groups). Then, you draw a random sample from each subgroup.</p><p data-sourcepos="5:1-5:116">Choosing sample sizes from these subgroups is where proportional and disproportional stratified sampling differ:</p><ul data-sourcepos="7:1-14:0"><li data-sourcepos="7:1-10:0"> <p data-sourcepos="7:3-7:359"><strong>Proportional Stratified Sample:</strong> Here, the sample size within each subgroup reflects its proportion in the entire population. Imagine voters are 60% young, 30% retirees, and 10% working professionals. In a sample of 100, you'd take 60 from young voters, 30 from retirees, and 10 from professionals. This ensures all groups are represented proportionally.</p> <ul data-sourcepos="9:3-10:0"><li data-sourcepos="9:3-10:0"><strong>Example:</strong> A school administrator wants to survey students about a new lunch program. The school has 60% freshmen, 20% sophomores, 15% juniors, and 5% seniors. If the administrator wants a total sample of 200 students, they would take a proportional stratified sample: 120 freshmen, 40 sophomores, 30 juniors, and 10 seniors.</li></ul> </li><li data-sourcepos="11:1-14:0"> <p data-sourcepos="11:3-11:430"><strong>Disproportional Stratified Sample:</strong> This approach focuses on specific subgroups you want to study more intensely. Maybe you're particularly interested in the opinions of young voters (60% of the population). You could take a larger sample, say 70, from them, while taking a smaller sample, like 20 each, from retirees and professionals. This way, you get more data on the group of interest (young voters) for better analysis.</p> <ul data-sourcepos="13:3-14:0"><li data-sourcepos="13:3-14:0"><strong>Example:</strong> Researchers want to understand the movie preferences of different age groups. They know teenagers are a smaller population segment (15%) but have a strong influence on movie trends. So, they take a disproportional stratified sample: 40 teenagers, 30 young adults (25% of population), 20 middle-aged adults (40% of population), and 10 seniors (20% of population). This allows a more focused study on teenagers' preferences.</li></ul> </li></ul>

Research Methodology Stratified sampling method - proportional stratified sample and disproportional stratified sample

The stratified sampling method is used when the population is heterogeneous rather than homogeneous. A heterogeneous population is composed ...

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Systematics Sampling -Criteria to choose -selecting a systematic sample

<p>In systematic sampling the sample units are selected from the population at equal intervals in terms of time, space or order. The selection of a sample using systematic sampling method is very simple. </p><p>From a population of ‘N’ units, a sample of ‘n’ units may be selected by following the steps given below:</p><ol style="text-align: left;"><li>Arrange all the units in the population in an order by giving serial numbers from 1 to N.</li><li>Determine the sampling interval by dividing the population by the sample size. That is, K=N/n.</li><li>Select the first sample unit at random from the first sampling interval (1 to K).</li><li>Select the subsequent sample units at equal regular intervals.</li></ol><p> <b>For example:</b></p><ol style="text-align: left;"><li>we want to have a sample of 100 units from a population of 1000 units. First arrange the population units in some serial order by giving numbers from 1 to 1000. </li><li>The sample interval size is K=1000/100=10. Select the first sample unit at random from the first 10 units ( i.e. from 1 to 10). </li><li>Suppose the first sample unit selected is 5, then the subsequent sample units are 15, 25, 35,.........995. </li><li>Thus, in the systematic sampling the first sample unit is selected at random and this sample unit in turn determines the subsequent sample units that are to be selected</li></ol><p><b>Advantages</b></p><p>i) The main advantage of using systematic sample is that it is more expeditious to collect a sample systematically since the time taken and work involved is less than in simple random sampling. For example, it is frequently used in exit polls and store consumers.</p><p style="text-align: justify;">ii) This method can be used even when no formal list of the population units is available. For example, suppose if we are interested in knowing the opinion of consumers on improving the services offered by a store we may simply choose every kth (say 6th) consumer visiting a store provided that we know how many consumers are visiting the store daily (say 1000 consumers visit and we want to have 100 consumers as sample size).</p><p style="text-align: justify;"><b>Limitations</b></p><p style="text-align: justify;">i) If there is periodicity in the occurrence of elements of a population, the selection of sample using systematic sample could give a highly unrepresentative sample. For example, suppose the sales of a consumer store are arranged chronologically and using systematic sampling we select sample for 1st of every month. The 1st day of a month can not be a representative sample for the whole month. Thus in systematic sampling there is a danger of order bias.<br /></p><p>ii) Every unit of the population does not have an equal chance of being selected and the selection of units for the sample depends on the initial unit selection. Regardless how we select the first unit of sample, subsequent units are automatically determined lacking complete randomness. </p>

Research Methodology Systematics Sampling -Criteria to choose -selecting a systematic sample

In systematic sampling the sample units are selected from the population at equal intervals in terms of time, space or order. The selection ...

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Kerala SET July 2024 registration process begins at lbscentre.kerala.gov.in

<p> LBS Centre for Science & Technology has begun the registration process for Kerala SET July 2024. The registration process will conclude on April 15 and the deadline for submitting the application form is April 17. Interested candidates can apply online through the official website at lbsedp.lbscentre.in.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHraMQ6voOmOS2b7MTuT7TQEl2n4G8HOqKCBpTjnRkZLmpCXaXeRd60SGhwgMbABvy5-C8blz9k2541SmibtUf4_WZu2nB0XcDbg5dZjZBg3fyRogoo5lsUS4vr_96ZG8HoOayTVAZgKHqW1jPps3WlEnQvIbP74nABXyIiRsdHCejTki8otBL4j2unNzS/s700/Top-5-BBA-Entrance-Exams.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="394" data-original-width="700" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHraMQ6voOmOS2b7MTuT7TQEl2n4G8HOqKCBpTjnRkZLmpCXaXeRd60SGhwgMbABvy5-C8blz9k2541SmibtUf4_WZu2nB0XcDbg5dZjZBg3fyRogoo5lsUS4vr_96ZG8HoOayTVAZgKHqW1jPps3WlEnQvIbP74nABXyIiRsdHCejTki8otBL4j2unNzS/s320/Top-5-BBA-Entrance-Exams.jpg" width="320" /></a></div><br /><p></p><p>Candidates can edit their Kerala SET July 2024 applications from April 18 to April 20.</p> <h2><a class="manualbacklink" href="https://lbsedp.lbscentre.in/setjul24/" rel="nofollow" target="_blank">Direct link to apply for Kerala SET July 2024</a></h2><h2>Kerala SET July 2024 examination pattern:</h2><p>There shall be two papers for the SET- JULY - 2024 . Paper I is common for all candidates. It consists of two parts, Part (A) General Knowledge and Part (B) Aptitude in Teaching. Paper II shall be a test based on the subject of specialisation of the candidate at the Post Graduate (PG) Level. There shall be 31 subjects for Paper II of the SET-JULY-2024.</p> <h2>Kerala SET July 2024: Know how to apply</h2><p>Visit the official website of LBS Centre for Science & Technology at lbscentre.kerala.gov.in</p> <p>On the homepage, click on the State Eligibility Test July 2024</p> <p>Register and proceed with the application</p> <p>Upload all the required documents and photograph</p> <p>Pay the application fee</p><p> </p>

K SET notifications Kerala SET July 2024 registration process begins at lbscentre.kerala.gov.in

 LBS Centre for Science & Technology has begun the registration process for Kerala SET July 2024. The registration process will conclud...

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Simple Random Sampling -Criteria to choose -selecting a random sample

<p>The easy random sample method is the one that is used most often. Every item in the community has an equal chance of being in the sample. This is called a simple random sample. Another thing is that choosing one thing for the sample shouldn't affect choosing another thing in any way. If the population is homogeneous, which means that all of the things in it have the same qualities that the researcher is interested in, then simple random picking should be used. Some of the things that can make people homogeneous are their age, gender, income, political, religious, or social beliefs, where they live, and so on.</p><p>A random number table is the best way to pick a simple random sample. The following things should be true of a random picking method.</p><ul style="text-align: left;"><li>There must be an equal chance for every person in the community to be in the sample.</li><li>The choice of one member is not changed by the choices of the members who came before.</li></ul><p>The random numbers are a set of digits that are made by a system that uses probability. The random numbers are made up of the following:</p><ul style="text-align: left;"><li>There is an equal chance of each number (0,1,2,3,4,5,6,7,8,or 9) showing up anywhere. That's one tenth.</li><li>It doesn't matter where any two digits appear; they can happen in any two places. </li></ul> To select a random sample using simple random sampling method we should follow the steps given below:<ol style="text-align: left;"><li>Determine the population size (N).</li><li>Determine the sample size (n).</li><li>Number each member of the population under investigation in serial order. Suppose there are 100 members number them from 00 to 99.</li><li>Determine the starting point of selecting sample by randomly picking up a page from random number tables and dropping your finger on the page blindly.</li><li>Choose the direction in which you want to read the numbers (from left to right, or right to left, or down or up).</li><li>Select the first ‘n’ numbers whose X digits are between 0 and N. If N =100 then X would be 2, if N is a four digit number then X would be 3 and so on.</li><li>Once a number is chosen, do not use it again.</li><li>If you reach the end point of the table before obtaining ‘n’ numbers, pick another starting point and read in a different direction and then use the first X digit instead of the last X digits and continue until the desired sample is selected.</li></ol><p><b><span style="color: red;">Advantages</span></b><br /></p><ol style="text-align: left;"><li>The simple random sample requires less knowledge about the characteristics of the population.</li><li>Since sample is selected at random giving each member of the population equal chance of being selected the sample can be called as unbiased sample. Bias due to human preferences and influences is eliminated.</li><li>Assessment of the accuracy of the results is possible by sample error estimation.</li><li>It is a simple and practical sampling method provided population size is not large.</li></ol><p style="text-align: left;"><b><span style="color: #cc0000;">Limitations</span></b><br /></p><ol style="text-align: left;"><li style="text-align: left;">If the population size is large, a great deal of time must be spent listing and numbering the members of the population.</li><li>A simple random sample will not adequately represent many population characteristics unless the sample is very large. That is, if the researcher is interested in choosing a sample on the basis of the distribution in the population of gender, age, social status, a simple random sample needs to be very large to ensure all these distributions are representative of the population. To obtain a representative sample across multiple population attributes we should use stratified random sampling.</li></ol>

Research Methodology Simple Random Sampling -Criteria to choose -selecting a random sample

The easy random sample method is the one that is used most often. Every item in the community has an equal chance of being in the sample. Th...

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Probability and Non-probability sampling methods

<p> In the field of Statistics, the sampling method, also known as the sampling methodology, refers to the systematic process of collecting and evaluating data in order to research a population. The data serves as the foundation, with a vast sample space. <br /><br />There are several sampling strategies that may be categorized into two classes. All these sampling strategies may require particularly targeting hard-to-reach or difficult-to-approach groups. <br /></p><h3 style="text-align: left;">Sampling Methods</h3>Statistics offers several sampling approaches to obtain meaningful data from a population. There are two distinct types of sampling methods:<br /><ol style="text-align: left;"><li> Sampling method based on the principles of probability theory.</li><li> Non-probability sampling refers to a method of selecting participants for a study that does not include random selection.</li></ol><h3 style="text-align: left;"> 1. Probability Sampling Overview</h3><ul style="text-align: left;"><li>Probability sampling is a method that uses random selection to ensure a representative sample. </li><li>It is more time-consuming and expensive than non-probability sampling but provides a reliable sample.</li></ul><p>Types of Probability Sampling<br /></p><ol style="text-align: left;"><li><b>Simple Random Sampling:</b> Every item in the population has an equal chance of being selected in the sample.</li><li><b>Systematic Sampling:</b> Items are selected from the target population by selecting the random selection point and selecting other methods after a fixed sample interval.</li><li><b>Stratified Sampling</b>: The total population is divided into smaller groups based on certain characteristics.</li><li><b>Clustered Sampling:</b> Clusters or groups of people are formed from the population set with similar significatory characteristics. Example: Random sampling can be used to select three or four branches as clusters for data collection.</li></ol><h3 style="text-align: left;"> 2. Non-Probability Sampling Methods Overview</h3><p>a) Convenience or Haphazard Sampling:<br /></p><ul style="text-align: left;"><li>Assumes all population units are identical, allowing any unit to be selected for the sample.</li><li>Example: vox pop survey where the interviewer selects any person who happens to walk by.</li><li>Selection subject to the biases of the interviewer and whoever happened to walk by at the time of sampling.</li></ul>b) Volunteer Sampling:<ul style="text-align: left;"><li>Respondents are volunteers, usually screened to get a set of characteristics suitable for the survey.</li><li>Can be subject to large selection biases, but sometimes necessary.</li><li>Example: callers to a radio or television show, where only those who care strongly enough about the subject tend to respond.</li><li>Often used for focus groups or in-depth interviews.</li></ul><p>C) Judgement Sampling:<br /></p><ul style="text-align: left;"><li>Sampling done based on previous ideas of population composition and behavior.</li><li>An expert with knowledge of the population decides which units in the population should be sampled.</li><li>Subject to the researcher’s biases and potentially more biased than haphazard sampling.</li><li>Can be useful in exploratory studies, such as selecting members for focus groups or in-depth interviews.</li></ul><p>D) Quota Sampling:<br /></p><ul style="text-align: left;"><li> One of the most common forms of non-probability sampling.</li><li>Sampling done until a specific number of units (quotas) for various sub populations have been selected.</li><li>Quota sampling is preferable to other forms of non-probability sampling because it forces the inclusion of members of different sub populations.</li><li>Unlike stratified sampling, quota sampling uses a non-random method, leaving the interviewer to decide who is sampled.</li></ul><p>E) Snowball and Crowdsourcing in Research<br /></p><ul style="text-align: left;"><li>Snowball sampling is a method used to find rare or hard-to-reach populations, such as people with disabilities, homeless people, drug users, or non-organized groups.</li><li> However, some individuals or subgroups may not be included in the sample.</li></ul>

Research Methodology Probability and Non-probability sampling methods

 In the field of Statistics, the sampling method, also known as the sampling methodology, refers to the systematic process of collecting and...

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Characteristics of a good sample.

<p> It is important that the sampling results must reflect the characteristics of the population. Therefore, while selecting the sample from the population under investigation it should be ensured that the sample has the following characteristics:</p><ul style="text-align: left;"><li>A sample must represent a true picture of the population from which it is drawn.</li><li>A sample must be unbiased by the sampling procedure.</li><li>A sample must be taken at random so that every member of the population of data has an equal chance of selection.</li><li>A sample must be sufficiently large but as economical as possible.</li><li>A sample must be accurate and complete. It should not leave any information incomplete and should include all the respondents, units or items included in the sample.</li><li>Adequate sample size must be taken considering the degree of precision required in the results of inquiry. </li></ul><p>More ever,  A good sample is the foundation for drawing accurate conclusions about a population. Here are some key characteristics of a good sample:</p><div _ngcontent-ng-c4283937972="" class="container"></div><div _ngcontent-ng-c4283937972="" class="container"></div><ul data-sourcepos="3:1-12:0"><li data-sourcepos="3:1-4:0"> <p data-sourcepos="3:3-3:315"><strong>Representativeness:</strong> This is the most crucial aspect. A good sample accurately reflects the important characteristics of the entire population. For instance, if you're studying political preferences of a country, the age, gender, and geographic distribution of the sample should mirror the population's makeup.</p> </li><li data-sourcepos="5:1-6:0"> <p data-sourcepos="5:3-5:222"><strong>Adequacy:</strong> The sample size should be large enough to provide reliable estimates of the population parameters. There are statistical methods to determine the minimum sample size needed for a certain level of precision.</p> </li><li data-sourcepos="7:1-8:0"> <p data-sourcepos="7:3-7:271"><strong>Randomness:</strong> Ideally, samples should be selected randomly using probability sampling techniques. This ensures that every member of the population has an equal chance of being included. Random sampling helps to minimize bias and allows for statistical generalization.</p> </li><li data-sourcepos="9:1-10:0"> <p data-sourcepos="9:3-9:256"><strong>Measurable Units:</strong> The sampling frame, the source from which the sample is drawn, should contain a list of units with well-defined characteristics that can be measured. This allows researchers to identify and select appropriate members for the sample.</p> </li><li data-sourcepos="11:1-12:0"> <p data-sourcepos="11:3-11:263"><strong>Minimized Non-Response:</strong> Non-response bias occurs when a significant portion of the chosen sample doesn't participate in the study. A good sampling method minimizes non-response by employing techniques like reminders or incentives to encourage participation.</p> </li></ul>

Research Methodology Characteristics of a good sample.

 It is important that the sampling results must reflect the characteristics of the population. Therefore, while selecting the sample from th...

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Basic terms and concepts in sampling

<p> sampling is a fundamental concept in statistics that allows us to draw inferences about a large group (population) by studying a smaller, manageable group (sample).</p><div _ngcontent-ng-c4283937972="" class="container"></div><div _ngcontent-ng-c4283937972="" class="container"></div><ul data-sourcepos="3:1-14:0"><li data-sourcepos="3:1-4:0"> <p data-sourcepos="3:3-3:235"><strong>Statistical Population:</strong> This is the entire collection of individuals or items we're interested in studying. It could be all the students in a school, all the customers of a company, or all the tomatoes grown in a specific region.</p> </li><li data-sourcepos="5:1-6:0"> <p data-sourcepos="5:3-5:262"><strong>Sample:</strong> This is a subset of the population that is chosen to represent the whole. We use samples because studying the entire population can be impractical or impossible. The accuracy of our inferences depends on how well the sample reflects the population.</p> </li><li data-sourcepos="7:1-8:0"> <p data-sourcepos="7:3-7:246"><strong>Sampling Frame:</strong> This is a list or database that identifies all the members of the population. It serves as the source from which we select the sample. An ideal sampling frame includes everyone in the target population and avoids duplicates.</p> </li><li data-sourcepos="9:1-10:0"> <p data-sourcepos="9:3-9:313"><strong>Sampling Error:</strong> This is the discrepancy between a sample statistic (e.g., average income in the sample) and the corresponding population parameter (e.g., average income in the population). Sampling error is inevitable, but we can minimize it by using good sampling techniques and increasing the sample size.</p> </li><li data-sourcepos="11:1-12:0"> <p data-sourcepos="11:3-11:261"><strong>Sample Size:</strong> This refers to the number of individuals or items selected from the population. There's a trade-off between sample size and cost/effort. Larger samples generally lead to lower sampling errors but require more resources to collect and analyze.</p> </li><li data-sourcepos="13:1-14:0"> <p data-sourcepos="13:3-13:283"><strong>Non-Response:</strong> This occurs when potential participants in a study (e.g., survey) choose not to respond. Non-response can introduce bias if those who don't respond differ systematically from those who do. Researchers try to minimize non-response through reminders and incentives.</p> </li></ul><p> </p>

Research Methodology Basic terms and concepts in sampling

 sampling is a fundamental concept in statistics that allows us to draw inferences about a large group (population) by studying a smaller, ...

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