Name: choosing samples for reliable results
Uploaded: 2024-12-05T18:31:54.000Z
Duration: 1 min 23 s
Channel: JoVE

Yaygın örnekleme yöntemleri rastgele, yargılayıcı, sistematik, tabakalı ve küme örneklemesidir. 
Rastgele örneklemede, bir örnek, her biri eşit seçilme şansına sahip, tercihsiz bir popülasyondan rastgele seçilen bireyler tarafından elde edilir.
Aksine, yargısal örnekleme rastgele olmayan bir süreçtir. Burada, popülasyonla ilgili mevcut bilgilere dayanarak bir örneklem seçilir.
Sistematik örnekleme, bir popülasyondan düzenli aralıklarla birey seçme yöntemidir. Örneğin, 12 kişilik bir popülasyondan 4 kişilik bir örneklem büyüklüğü oluşturmak için, başlangıç noktası bir ile örnekleme aralığı arasında rastgele seçilir. Bu nedenle, her 3. üye 4 kişiden oluşan bir örneklem oluşturmak üzere seçilir.
Tabakalı örneklemede popülasyon, bireyler tarafından paylaşılan özelliklere göre alt gruplara veya tabakalara ayrılır. Bireyler daha sonra analiz için nihai bir örnek oluşturmak üzere her bir alt gruptan orantılı olarak seçilir.
Benzer şekilde, küme örneklemesi, popülasyonun alt gruplara veya kümelere bölünmesini de içerir. Daha sonra değerlendirme için rastgele birkaç küme seçilir.

Örnekleme planları

Örnekleme, araştırmacıların büyük bir popülasyondan temsili veriler toplamasına olanak tanıyan analitik kimyada önemli bir adımdır. Yaygın örnekleme yöntemleri arasında rastgele, yargısal, sistematik, katmanlı ve kümelenmiş örnekleme bulunmaktadır.

Rastgele örnekleme, popülasyondaki her numunenin örnekem için seçilme şansının eşit olduğu bir yöntemdir. Bu yöntem, numunelerin rastgele seçilmesini içerir ve genellikle rastegle sayı üreteçleri veya piyango tarzı yöntemler kullanılır. Örneğin, bir polimer levhanın özelliklerini analiz ederken, levha eşit 1 cm x 1 cm'lik bölümlere ayrılabilir ve rastgele bir sayı tablosu kullanılarak analiz için rastgele 10 bölüm seçilebilir. Bu, levha boyunca tarafsız örneklerin seçilmesini sağlar. Rastgele örnekleme, önyargıyı en aza indirir ve tüm popülasyonu doğru bir şekilde temsil eden bir örnek elde edilmesine yardımcı olur.

Amaçlı veya seçici örnekleme olarak da bilinen yargısal örnekleme, araştırmacının yargısına veya uzmanlığına dayanarak çalışma için en uygun görülen bireyleri veya örnekleri seçmeye dayanan olasılık dışı bir yöntemdir. Örneğin, bir araştırmacının balıklardaki poliklorlu bifenillerin (PCB'ler) biyolojik birikimini incelediğini varsayalım. Bu durumda, daha küçük olmak veya sağlıksız görünmek gibi belirli kriterleri karşılayan balıkları seçici olarak örnekleyebilirler. Bu yöntem, düzenleyici kurumların toplanan örneği tanımlamak için belirli kriterler talep etmesi durumunda sıklıkla kullanılır.

Sistematik örnekleme, rastgele bir başlangıç noktası seçildikten sonra, popülasyondan her n'inci bireyi seçmeyi içerir. Örneğin, çevresel çalışmalarda, sistematik örnekleme bir gölden örnekler toplamak için kullanılabilir; göl bir ızgaraya bölünerek her bir bölümden düzenli aralıklarla örnekler alınır, böylece gölün her bölgesinden örnekleme yapılır ve mekansal kapsam sağlanır. Bu yaklaşım, suyun oksijen dağılımı gibi mekansal eğilimleri içeren çalışmalarda yardımcı olur.

Katmanlı örnekleme, popülasyonu araştırmaya yönelik belirli özelliklere dayalı olarak farklı alt gruplara veya katmanlara ayırmayı içerir. Ardından, her katmandan, popülasyondaki temsiline orantılı olarak rastgele örnekler seçilir. Örneğin, kentsel havadaki partikül maddeyi incelerken, katmanlı örnekleme, popülasyonu partikül boyutu kategorilerine (örneğin, ince, kaba) ayırarak kullanılabilir. Ardından, her partikül boyutu grubundan örnekler alınarak bu grupların genel kirlilik seviyelerine katkısı değerlendirilir. Bu yöntem, her alt grup içinde daha hassas bir analiz sağlar ve örnekleme hatasını azaltır.

Kümelenmiş örneklemese, popülasyonu kümelere veya gruplara, ideal olarak popülasyonun mini temsillerine ayırmayı içerir. Birkaç küme rastgele seçilir ve seçilen kümelerdeki herkes örneklenir. Bu yöntem, lojistik kısıtlamaların örnekleme alanlarının sayısını sınırladığı büyük ölçekli çalışmalarda yaygın olarak kullanılır. Örneğin, yeraltı suyu kirliliği üzerine bir çalışma, bir bölgeyi coğrafi sınırlara göre kümelere bölebilir ve ardından tüm mevcut kuyuları örneklemek için birkaç kümeyi (bölgeyi) rastgele seçebilir.

Örnekleme Planları

Örnekleme, analitik kimyada çok önemli bir adımdır ve araştırmacıların büyük bir popülasyondan temsili veriler toplamasına olanak tanır. Yaygın örnekleme yöntemleri ...

Education

JoVE Core

Analytical Chemistry

Analitik Kimya

Unit 1

Ünite 1

Method Development and Sampling Techniques

Yöntem Geliştirme ve Örnekleme Teknikleri

method-development-and-sampling-techniques

KEY TERMS AND CONCEPTS

TEMEL TERİMLER VE KAVRAMLAR

Sampling Plans

choosing samples for reliable results

Sampling is a key step in analytical chemistry. It helps researchers collect data that represents a larger population. The main sampling methods in this topic are random, judgmental, systematic, stratified, and cluster sampling.

Random sampling gives each member of the population an equal chance of being chosen. Researchers often use a random number generator or a lottery-style method. For example, a polymer sheet can be divided into 1 cm x 1 cm sections, and 10 sections can be selected at random for analysis. This helps reduce bias and supports a sample that reflects the whole population.

Judgmental sampling is also called purposive or selective sampling. It depends on the researcher&amp;#39;s judgment to choose samples that fit the study. For example, a researcher studying bioaccumulation of PCBs in fish may choose smaller fish or fish that appear unhealthy. Regulatory agencies may also require specific criteria for this kind of sampling.

Systematic sampling uses a random starting point and then selects every n th individual. It can also be used in environmental studies with a grid, such as sampling a lake at regular intervals. This method helps provide spatial coverage across the lake. It is useful when studying spatial trends, such as oxygen distribution in water.

Stratified sampling divides a population into strata, or subgroups, based on a feature linked to the study. Samples are then randomly taken from each stratum in proportion to its size in the population. For example, particulate matter in urban air can be grouped by particle size, such as fine and coarse particles. This approach can improve precision within each subgroup and reduce sampling error.

Cluster sampling divides the population into clusters, or groups that can act as mini-representations of the whole. A few clusters are randomly selected, and everyone within those clusters is sampled. This method is often used in large studies when the number of sampling sites must be limited. For example, a groundwater contamination study may divide a region into geographic clusters and then sample all available wells in the chosen clusters.

Choosing Samples for Reliable Results

development of analytical methods

Analytical Methods: Technique to Protocol

Analytical methods follow a clear path from technique to protocol. In analytical chemistry, a technique is the scientific principle used to explain a chemical measurement. A method applies that technique to a sample of interest. The next steps are procedure and protocol. A procedure gives the directions for carrying out the analysis with an analytical method. A protocol is the detailed set of guidelines for that procedure, and it should be followed closely to get the desired results. <br...

quality control

Quality Control Charts and Lab Records

Quality control charts, proficiency testing, and lab records are key parts of quality control in a laboratory. These steps help keep a system under statistical control. They also help track data, show trends, and spot variations that may affect the accuracy of an analysis. One common tool is a quality control chart. This chart plots one or more analytical characteristics over time. The measured values are taken at different times during the analysis and compared with the known value of...

quality assurance

Quality Checks in Analytical Testing

Quality assurance in analytical testing brings together the checks that help a system perform properly. It includes quality control, quality assessment, and internal corrective measures. These parts work in a cycle to support reliable results. Quality control happens before and during the analysis. Quality assessment takes place during and after the investigation. Internal corrective measures are then based on the results of post-analysis quality assessment, or they may be applied before a new...

data validation

How Analytical Methods Are Checked

Method validation checks whether an analytical chemistry method gives reliable results. It is especially important when the same method is used with different sample matrices or when the procedure is changed. Validation helps show that the method still meets acceptable standards in each case. Several key checks are used in method validation. Specificity means the method can measure the target analyte without interference from other substances. Linearity means the result changes in direct...

qualitative analysis

Classical and Instrumental Sample ID

Qualitative analysis identifies the elements, ions, or compounds in an unknown sample. It is the first step in the hierarchy of analytical goals. That makes it the starting point for scientific study before quantitative analysis or other forms of analysis. Two main approaches are used in qualitative analysis: classical methods and instrumental methods. Classical methods depend on the visible or sensory result of a chemical reaction, such as a color change, a precipitate, or even a smell. These...

quantitative analysis

Measuring Analyte Concentration

Quantitative analysis measures the amount of a specific substance, called the analyte, in a sample. If the sample composition is unknown, qualitative analysis comes first. That step identifies the components so the correct substances can be measured during the quantitative phase. This method depends on two key measurements. Scientists measure the sample quantity and also a property that is proportional to the amount of analyte. Together, these measurements help determine the analyte...

instrument calibration

Calibrating Lab Instruments for Accurate Results

Calibration helps lab instruments produce accurate and consistent results. It is important in manufacturing, healthcare, testing laboratories, and scientific research. Each instrument needs a calibration process that matches its design and function. This process improves data accuracy and helps results stay reliable. An analytical balance measures mass and needs regular calibration to stay precise. It is calibrated by comparing its readings with known standard weights. These weights act as...

glassware calibration

Calibrating Lab Glassware for Accurate Volume

Calibrating lab glassware helps students measure liquids accurately in the analytical laboratory. It reduces volume errors and supports consistent results across measurements. This matters when using volumetric flasks, pipettes, burettes, and other tools that depend on precise volume readings. Volumetric flasks are used to prepare aqueous solutions with a precise final volume. They have a calibration line on the neck. To calibrate one, fill it with distilled water until the meniscus, the...

standard solutions

Preparing Standard Solutions in the Lab

Standard solutions are prepared to have a precisely known concentration or composition. In analytical chemistry, this accuracy is important because it supports reliable results. A primary standard is a highly pure, stable substance with a high molar mass. It is fully soluble in water, which is the most commonly used solvent in analytical chemistry. A primary standard solution can be used to standardize a secondary standard. Secondary standards have known concentrations, but they are usually...

blank solutions

Blank Solutions in Analytical Testing

Blank solutions are used in analytical testing to show the background signal before a sample is measured. A blank solution does not contain the analyte, which is the substance of interest being tested or measured. It is usually made with the same reagents and procedure as the sample solution, but without the analyte. The main purpose of a blank solution is to control for background interference and contamination. Reagents, solvents, or lab equipment can sometimes create a detectable signal on...

sampling methods: overview

How Sampling Shapes Lab Results

Sampling shapes lab results by determining how a smaller sample represents a larger population. In analytical chemistry, testing an entire population is often impractical or impossible, so samples are collected to make inferences about the whole group. The sampling method is the process used to choose individuals or items for that sample. Common sampling methods include random, judgemental, systematic, stratified, and cluster sampling. Each method can affect the quality of the data in a...

sampling methods: sample types

Sampling Materials in Solid, Liquid, Gas

Sampling materials are grouped as solid, liquid, or gas. The type of sample helps decide the right collection method. Solid samples can include sediments from water bodies, soil, metals, and biological tissues. Sediment sampling from water bodies can be done in two common ways. Grab sampling uses a device to collect a discrete sample from the bottom with minimal disturbance. These samples may not show the full area because conditions can vary from place to place. Piston coring collects a...

sampling plans

Sampling is a key step in analytical chemistry. It helps researchers collect data that represents a larger population. The main sampling methods in this topic are random, judgmental, systematic, stratified, and cluster sampling. Random sampling gives each member of the population an equal chance of being chosen. Researchers often use a random number generator or a lottery-style method. For example, a polymer sheet can be divided into 1 cm x 1 cm sections, and 10 sections can be selected at...

sample preparation for analysis: overview

Preparing Samples by State and Form

Sample preparation depends on the state and form of the sample before analysis. It helps make the sample ready for accurate measurement. The main goal is to extract the analyte, which is the substance being measured, and remove parts that could interfere with the analysis. Solid samples often need the most handling. Large solids are usually ground, crushed, or milled to make them smaller and increase surface area. The sample is then sieved and mixed so it has a uniform composition. After that,...

sample preparation for analysis: advanced techniques

sample-preparation-for-analysis-advanced-techniques

Preparing Complex Samples for Analysis

Preparing complex samples for analysis often requires special digestion and fusion methods. Samples with inorganic or organic material can be hard to dissolve or break down. Common sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion. These steps help turn difficult samples into forms that can be tested reliably and again with similar results. Acid digestion is often used for inorganic materials that do not dissolve in water. It is useful for soil and...

sample handling

Sample Transport and Storage Basics

Sample transport and storage help keep specimens intact before testing. Moving a sample from the collection point to the laboratory requires careful handling. The sample should be sealed well and clearly labeled. These steps help prevent cross-contamination and support reliable results. Temperature control is also important during transport. Some samples travel in insulated containers, cool packs, or refrigerated vehicles so they stay within the needed temperature range. After the samples...

contaminants and errors

Sampling Errors in Sample Preparation

Sampling errors in sample preparation can affect the accuracy of laboratory results. Two common problems are concentration bias from poor sample splitting and contamination from reducing particle size by grinding or homogenization. Recognizing these errors helps make analysis more reliable. The number of samples also matters when a target population follows a normal distribution. In that case, the sample size ns can be found with a formula that uses the t-value for the chosen confidence level,...

key terms and concepts

Learn how random, judgmental, systematic, stratified, and cluster sampling are used in analytical chemistry to collect representative data.

Choosing Samples for Reliable Results | JoVE K12

JoVE Core Analitik Kimya, karmaşık kimyasal prensiplerini anlaşılır, ilgi çekici animasyonlara dönüştürerek basitleştirir ve SI Birimlerinden NMR spektroskopisine kadar konuları daha erişilebilir ve anlaşılır hale getirir. Buna ek olarak, bilim insanlarının yer aldığı videolar, bu kavramların dünya çapındaki laboratuvarlarda yürütülen gerçek dünya araştırmalarındaki uygulamalarını gösterir.

analytical chemistry

Choosing Samples for Reliable Results

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