2.2 Experimental units and sampling techniques
3 min read•august 7, 2024
Experimental units and sampling techniques are crucial for designing effective experiments. They determine how we select subjects and apply treatments, impacting the validity of our results. Understanding these concepts helps us create robust studies that accurately represent populations.
Sampling methods like simple random, stratified, and ensure we get representative samples. Meanwhile, experimental units define how we apply treatments and measure outcomes. These elements form the foundation for reliable experimental design and data analysis.
Sampling Techniques
Probability Sampling Methods
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- Simple
- Each unit in the has an equal chance of being selected
- Randomly select units from the population using a random number generator or random number table
- Ensures the sample is representative of the population and minimizes
- Works best for homogeneous populations where each unit is similar to the others
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- Divide the population into distinct subgroups or strata based on a specific characteristic (age, gender, income level)
- Randomly select units from each stratum in proportion to their representation in the population
- Ensures all subgroups are adequately represented in the sample
- Increases precision and reduces sampling error compared to simple random sampling
- Cluster sampling
- Divide the population into clusters or based on geographic location or other naturally occurring groupings
- Randomly select a subset of clusters to include in the sample
- Sample all units within the selected clusters
- Cost-effective and efficient for large, geographically dispersed populations
- May introduce sampling bias if clusters are not representative of the population
Systematic Sampling
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- Select units from the population at a fixed interval (every 10th unit on a list)
- Determine the sampling interval by dividing the population size by the desired
- Randomly select a starting point and then select every nth unit thereafter
- Easy to implement and can be more precise than simple random sampling
- May introduce bias if there is a periodic pattern in the population that coincides with the sampling interval
Experimental Design Basics
Units and Population
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- The smallest unit to which a treatment is applied in an experiment
- Can be an individual subject (person, animal, plant) or a group of subjects (classroom, litter of mice)
- The experimental unit is the unit of replication and the basis for the analysis of the experiment
- Example: In a study on the effect of fertilizer on crop yield, each individual plot of land receiving a specific fertilizer treatment would be an experimental unit
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- The unit on which measurements or observations are made in an experiment
- May be the same as the experimental unit or a subunit of the experimental unit
- Example: In the crop yield study, if the yield is measured for each individual plant within a plot, then each plant is an observational unit
- Population
- The entire group of or units that the researcher wants to study and draw conclusions about
- Can be a population of people, animals, plants, objects, or events
- The goal of an experiment is to make inferences about the population based on the sample results
- Example: In a study on the effectiveness of a new drug, the population might be all adults with a specific medical condition
Sample Size Considerations
- Sample size
- The number of units selected from the population to be included in the study
- Determines the precision and power of the experiment
- Larger sample sizes generally lead to more precise estimates and a greater ability to detect significant differences between treatments
- Factors to consider when determining sample size include the variability of the population, the desired level of precision, and the available resources (time, budget)
- Statistical can be used to determine the minimum sample size needed to detect a specified effect size with a given level of significance
Key Terms to Review (22)
Bias: Bias refers to a systematic error that affects the validity of research results by skewing data in a particular direction. It can stem from various sources, such as the selection of participants, the design of the study, or the way data is collected and analyzed. Understanding bias is crucial for interpreting results accurately and ensuring that findings reflect true effects rather than distortions caused by flawed methodology.
Blocking: Blocking is a technique used in experimental design to reduce the impact of variability among experimental units by grouping similar units together. This method allows researchers to control for specific variables, ensuring that comparisons between treatment groups are more accurate and reliable. By minimizing extraneous variability, blocking can enhance the precision of the experiment and improve the validity of conclusions drawn from the data.
Cluster Sampling: Cluster sampling is a statistical technique where the population is divided into separate groups, known as clusters, and a random sample of these clusters is selected for study. This method is especially useful when the population is too large or spread out, as it allows for easier data collection while still maintaining a level of randomness and reducing costs associated with sampling.
Confounding Variable: A confounding variable is an extraneous factor that correlates with both the independent and dependent variables in an experiment, potentially leading to misleading conclusions about the relationship between them. This variable can create a false impression of causation, impacting the clarity and accuracy of the experimental results. Identifying and controlling for confounding variables is crucial for ensuring that findings accurately reflect the intended effects of the independent variable.
Control Group: A control group is a baseline group in an experiment that does not receive the experimental treatment or intervention, allowing researchers to compare it with the experimental group that does receive the treatment. This comparison helps to isolate the effects of the treatment and determine its effectiveness while accounting for other variables.
Experimental unit: An experimental unit is the smallest division of experimental material to which a treatment is independently applied. This term is crucial for ensuring that results are valid and reliable, as it allows researchers to accurately measure the effects of treatments on different subjects or objects. Understanding what constitutes an experimental unit helps in designing studies that control for variability and improve the precision of the results.
External Validity: External validity refers to the extent to which research findings can be generalized to, or have relevance for, settings, people, times, and measures beyond the specific conditions of the study. This concept connects research results to real-world applications, making it essential in evaluating how applicable findings are to broader populations and situations.
Field Experiment: A field experiment is a research study conducted in a natural setting where researchers manipulate one or more independent variables to observe their effect on a dependent variable while controlling for extraneous factors. These experiments are essential for testing hypotheses in real-world scenarios, allowing researchers to draw more generalizable conclusions about causal relationships in everyday environments.
Groups: In experimental design, 'groups' refer to distinct collections of experimental units that are subjected to different treatments or conditions in a study. The arrangement of these groups is crucial because it helps researchers determine the effects of independent variables on dependent variables while controlling for extraneous factors. Groups can be formed through various sampling techniques, which ensures that the results are both valid and reliable.
Individuals: In research, 'individuals' refer to the single units or subjects that are being observed or measured in a study. These units can be people, animals, or even objects depending on the context of the experiment. Understanding who the individuals are is crucial as it helps determine the sample size, sampling techniques, and the overall validity of the experimental results.
Laboratory experiment: A laboratory experiment is a controlled investigation conducted in a dedicated environment, where variables can be manipulated to observe their effects on experimental units. This type of experiment allows researchers to isolate specific factors and establish cause-and-effect relationships, making it a powerful tool for scientific inquiry. By using standardized procedures and equipment, laboratory experiments enhance the reliability and validity of findings while minimizing external influences.
Observational Unit: An observational unit is the smallest entity or subject that can be observed or measured in a study. It serves as the fundamental building block for data collection, as it is the individual element from which data is gathered and analyzed, whether it be a person, animal, object, or event. Understanding the observational unit is crucial for designing experiments and choosing appropriate sampling techniques to ensure valid and reliable results.
Plots: In experimental design, plots refer to the specific areas or units where treatments are applied during an experiment. Plots can vary in size and shape, depending on the nature of the experiment and the sampling techniques employed. They play a crucial role in how data is collected and analyzed, as well as in determining the overall validity of the results obtained from an experiment.
Population: In research, a population refers to the entire group of individuals or items that share a common characteristic or trait that is being studied. This can include people, animals, plants, or objects, and is crucial for determining the scope of research and ensuring that findings can be generalized. Understanding the population helps in selecting appropriate experimental units and sampling techniques to draw valid conclusions.
Power analysis: Power analysis is a statistical method used to determine the likelihood that a study will detect an effect of a specified size, assuming that the effect actually exists. It connects sample size, significance level, and the expected effect size to help researchers ensure their study is adequately equipped to draw meaningful conclusions.
Random Assignment: Random assignment is a technique used in experimental research to ensure that participants are allocated to different groups or conditions in a way that is not influenced by any biases or pre-existing differences. This process helps to create equivalent groups, enhancing the credibility of the experiment's conclusions by minimizing confounding variables.
Random Sampling: Random sampling is a technique used in research to select a subset of individuals from a larger population in such a way that every individual has an equal chance of being chosen. This process is crucial for ensuring that the sample accurately represents the population, thereby enhancing the reliability and validity of experimental findings and conclusions drawn from them.
Replicability: Replicability refers to the ability of a study or experiment to be repeated by other researchers and achieve the same or similar results. This concept is crucial in ensuring that findings are reliable and not merely a result of random chance or specific conditions. When research is replicable, it reinforces the credibility of scientific claims and allows for the verification of methods and results across different settings and populations.
Sample Size: Sample size refers to the number of observations or data points included in a study, playing a critical role in the validity and reliability of research findings. It directly impacts the precision of estimates, the statistical power of tests, and the ability to generalize results to a larger population. A well-determined sample size ensures that research can detect meaningful effects while minimizing error and bias.
Stratified Sampling: Stratified sampling is a technique used to ensure that specific subgroups within a population are adequately represented in a sample. This method divides the population into distinct groups, or strata, based on shared characteristics, and samples from each stratum in proportion to its size or importance. It connects to various concepts by enhancing the accuracy of estimates, reducing sampling error, and facilitating statistical inference by allowing researchers to make more reliable conclusions about the entire population.
Systematic Sampling: Systematic sampling is a statistical technique where researchers select participants or experimental units from a larger population at regular intervals. This method ensures that the sample is spread evenly across the population, which can help to reduce bias and make the sample more representative. It connects to the concept of cluster sampling, as both methods focus on efficient sampling strategies while ensuring diversity within the selected units.
Treatment group: A treatment group is a subset of experimental units that receives a specific intervention or treatment in an experiment, allowing researchers to observe the effects of that treatment compared to a control group. Understanding the treatment group is crucial as it relates to how variables are manipulated and measured, randomization techniques, and methods for controlling variability in the data.