Name: random assignment in experiments
Uploaded: 2023-04-30T12:27:36.000Z
Duration: 1 min 12 s
Channel: JoVE

K12 Standards

Virginia  Standards of Learning

HIgh School

Chemistry

CH.1

CH.1.c

What is an Experiment?

Une expérience est une approche systématique visant à prouver ou à réfuter une hypothèse et à découvrir de nouvelles connaissances. Les statistiques sont souvent utilisées pour interpréter les résultats d’une expérience.
Prenons l’exemple d’une expérience visant à déterminer la relation entre l’utilisation du téléphone portable et la qualité du sommeil chez les étudiants.
Ces étudiants, appelés matières, sont divisés en deux groupes. L’un est expérimental, tandis que l’autre est un groupe témoin. Seul le groupe expérimental est autorisé à utiliser son téléphone portable trente minutes avant le coucher, tandis que le groupe témoin sert de référence de référence.
Les chercheurs enregistrent ensuite le nombre d’heures de sommeil et la latence ou les variables du sommeil - les caractéristiques des sujets qui sont examinées, mesurées et interprétées.
Les variables sont de deux types : dépendantes et indépendantes.
L’utilisation du téléphone portable est une variable indépendante contrôlée par les chercheurs, et les heures de sommeil et la latence du sommeil sont des variables dépendantes qui sont mesurées et enregistrées.
Enfin, des tests d’hypothèses sont utilisés pour déduire si les changements observés dans la qualité du sommeil dans le groupe expérimental par rapport au groupe témoin sont statistiquement significatifs.

qu'est-ce qu'une expérience ?

Une expérience est une étude méthodiquement planifiée et réalisée dans des conditions contrôlées. L'objectif d'une expérience est d'analyser la relation entre deux variables. Lorsqu’une variable influence une autre, on appelle la première variable la variable explicative (ou variable indépendante). La variable affectée est appelée variable de réponse (ou variable dépendante). Dans une expérience randomisée, le chercheur manipule les valeurs de la variable explicative et mesure les variations qui en résultent dans la variable de réponse. Les différentes modalités de la variable explicative sont appelées traitements. Une unité expérimentale correspond à un individu ou un objet unique sur lequel une mesure est effectuée. 

 Vous souhaitez évaluer l’efficacité de la vitamine E dans la prévention des maladies. Vous recrutez un groupe de sujets et leur demandez s’ils prennent régulièrement de la vitamine E. Vous observez que les individus consommant de la vitamine E présentent en moyenne un meilleur état de santé que ceux qui n’en prennent pas. Cela prouve-t-il que la vitamine E est efficace pour prévenir les maladies ? Non. Il existe de nombreux facteurs qui différencient ces deux groupes en dehors de la consommation de vitamine E. En effet, les personnes qui prennent régulièrement de la vitamine E adoptent souvent d’autres comportements favorables à la santé : activité physique, alimentation équilibrée, consommation d’autres compléments alimentaires, et non-tabagisme. Chacun de ces éléments peut influencer l’état de santé des individus. Dans ces conditions, cette étude ne permet pas de conclure que la vitamine E est responsable de la prévention des maladies. 

 Les variables supplémentaires qui peuvent fausser une étude sont appelées variables confondantes (ou variables de confusion). Afin de démontrer que la variable explicative est à l'origine des variations observées dans la variable de réponse, il est essentiel d’isoler son effet. Pour cela, le chercheur doit concevoir son expérience de manière à ce qu'une seule différence existe entre les groupes comparés : les traitements attribués. Cet objectif est atteint grâce à l'affectation aléatoire des unités expérimentales aux groupes de traitement. Lorsque l’attribution des traitements est réalisée aléatoirement, toutes les variables confondantes potentielles sont réparties de manière équitable entre les groupes. À ce stade, la seule différence entre les groupes est celle imposée par le chercheur. Ainsi, les variations observées dans la variable de réponse doivent être directement imputables aux traitements appliqués. C’est de cette manière qu’une expérience permet d’établir un lien de causalité entre la variable explicative et la variable de réponse.

 Ce texte est adapté de <a href="https://openstax.org/books/introductory-statistics/pages/1-4-experimental-design-and-ethics">Openstax, Introductory Statistics, Section 1.4 </a><a href="https://openstax.org/books/introductory-statistics/pages/1-4-experimental-design-and-ethics">Experimental Design and Ethics</a>

Qu'est-ce qu'une expérience ?

Une expérience est une activité planifiée réalisée dans des conditions contrôlées. Le but d’une expérience est d’étudier la relation entre deux ...

Education

Enseignement

JoVE Core

Statistics

Statistiques

Unit 1

Unité 1

Statistics in Practice

Les statistiques en pratique

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

random assignment in experiments

Random assignment in experiments helps researchers test cause and effect between two variables. An experiment is a planned activity carried out under controlled conditions. It is used to study how one variable affects another. The variable that causes change is the explanatory or independent variable. The variable that changes in response is the response or dependent variable.

In a randomized experiment, the researcher changes the explanatory variable and measures the response variable. The different values of the explanatory variable are called treatments. An experimental unit is the single object or individual that is measured. This structure lets the researcher compare treatment groups under the same planned conditions.

A simple health example shows why random assignment matters. Suppose a researcher wants to test whether vitamin E helps prevent disease. If people are only asked whether they already take vitamin E, the group that takes vitamin E may look healthier on average. But that result does not prove vitamin E is the reason.

Other differences can affect the outcome. People who take vitamin E regularly may also exercise more, eat better, take other vitamin supplements, or choose not to smoke. These extra influences are called lurking variables. They can cloud a study and make it hard to tell which factor is really causing the change.

To isolate the explanatory variable, the researcher must design the experiment so that the treatment is the only planned difference between the groups. This is done by random assignment of experimental units to treatment groups. Random assignment spreads potential lurking variables more equally among the groups. Then differences in the response variable can be linked to the treatments themselves.

Random Assignment in Experiments

what is an experiment?

Random assignment in experiments helps researchers test cause and effect between two variables. An experiment is a planned activity carried out under controlled conditions. It is used to study how one variable affects another. The variable that causes change is the explanatory or independent variable. The variable that changes in response is the response or dependent variable. In a randomized experiment, the researcher changes the explanatory variable and measures the response variable. The...

study design in statistics

Experimental and Observational Study Types

Study design in statistics helps researchers collect and analyze data from variables in a specific research problem. It supports stronger experiments and more reliable data. Questions about aspirin and heart attacks, fertilizer and rose growth, or fatigue and alcohol-related driving risk all depend on careful study design. Study designs are usually grouped into two main types: descriptive and analytical studies. Descriptive studies describe the variables and answer what is happening, rather...

observational studies

Types of Observational Research Methods

Observational research methods let scientists study events without changing them. The researcher does not intervene, so the response variable and the outcome are left alone. These studies are used to look for patterns and relationships in real-world settings. There are three main types of observational studies: prospective, retrospective, and cross-sectional. Each type collects information in a different way. Together, they help researchers answer questions about health, behavior, and risk. <br...

experimental designs

Types of Experimental Design

Experimental design is a systematic way to study how an independent variable affects a dependent variable. Researchers use it to compare results and judge whether a treatment or intervention changes the outcome. Three common experimental designs are pre-experimental design, true experimental design, and quasi-experimental design. Each one supports research in a different way, depending on how groups are formed and how the data are collected. In pre-experimental design, the researcher compares...

randomized experiments

How Random Assignment Reduces Bias

Random assignment helps researchers place participants into experimental and control groups. It gives each person an equal chance of being assigned to either group. This process is used to reduce selection bias and to balance known and unknown confounding factors, so the control group is as similar to the treatment group as possible. A computer program or a random number generator can make the assignment process less biased. Simple randomization is the most direct method. It uses a single...

crossover experiments

Repeated-Measures Study Design

Crossover experiments are a repeated-measurements study design used in science and medicine. In this design, the same experimental units receive all treatments during different time periods. That setup helps compare how each treatment performs within the same group. These studies are used in psychology, the pharmaceutical industry, agriculture, and medicine. They can work with smaller sample sizes because the samples act as their own controls. That makes crossover designs useful when...

controls in experiments

Why Control Groups Improve Experiment Results

Control groups help experiments produce more reliable results. They reduce bias and make it easier to measure the dependent variable. In an experiment, controls have the same features as the treatment groups but are not affected by the independent variable. Researchers sort data into control and experimental conditions to see the relationship between the independent and dependent variables. A randomized experiment includes a control group that receives an inactive treatment. The control group...

bias

Biased Sampling and Fair Tests

Bias is any tendency that keeps a question from being unprejudiced. In research, bias appears when one outcome or answer is favored over others during sampling or testing. It can happen at many stages, including study design, data collection, analysis, and publication. A common form is sampling bias. This happens when a sample is taken from a population and some members are less likely to be chosen than others. In a fair sample, each member of the population should have an equally likely...

blinding

Placebos and Bias in Randomized Trials

Placebos and blinding are key tools in randomized control trials, or RCTs. They help researchers reduce bias, which can affect study results. In a blinded study, participants do not know which treatment they are receiving. A placebo is a dummy treatment that does not contain the active treatment. Researchers use a placebo in the control group to compare against the treatment group. This helps balance the effects of being in an experiment with the effects of the active treatment itself. A...

clinical trials

How Clinical Trials Test New Treatments

Clinical trials test new treatments in people to check safety and effectiveness. These experimental studies can look at drugs, diet methods, and medical devices. Researchers use statistics to turn collected data into reasonable and accurate conclusions, so they can make sound decisions when results are uncertain. Statistics also help medical researchers reduce error and bias. That matters because clinical trial data can be influenced by chance or by uneven study design. Careful analysis...

key terms and concepts

Learn how randomized experiments use treatments, experimental units, and random assignment to test cause and effect while reducing lurking variables.

Random Assignment in Experiments | JoVE K12

statistics

JoVE Core Statistiques introduit aux étudiants les concepts suivants et leurs applications: la collection, l'analyse, la présentation, et l' interprétation des données. En parallèle, les scientifiques à l'action démontrent l’application de ces concepts dans des expériences de recherche classiques et originales réalisées dans des laboratoires du monde entier.

Physical Science

PS.1

PS.1.b

Physics

PH.1

PH.1.b

Texas Essential Knowledge and Skills

High School

CHEM.1

CHEM.1.B

Florida Department of Education

Nature of Science

SC.912.N.2

SC.912.N.2.2

PHYS.1

PHYS.1.B

Chemistry and Physics

IPC.1

IPC.1.B

The Scientific Method

The scientific method is a detailed, empirical problem-solving process used by biologists and other scientists. This iterative approach involves ...

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in ...

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the ...

Correlation and Causation

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The student will demonstrate an understanding of scientific and engineering practices by asking questions and defining problems
• ask questions that arise from careful observation of phenomena, examination of a model or theory, unexpected results, and/or to seek additional information
• determine which questions can be investigated within the scope of the school laboratory 
• make hypotheses that specify what happens to a dependent variable when an independent variable is manipulated
• generate hypotheses based on research and scientific principles 
• define design problems that involve the development of a process or system with interacting components, criteria and constraints

CH.1.a

Case Studies

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Surveys

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Archival Research

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Longitudinal Research

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Cross-Sectional Research

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Group Design

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Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable ...

The Placebo Effect

The placebo effect occurs when people&#39;s expectations or beliefs influence or determine their experience in a given situation. In other words, simply ...

Blind Procedures

Ideally, the people who observe and record the children&rsquo;s behavior are unaware of who was assigned to the experimental or control group, in order to ...

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not ...

The student will demonstrate an understanding of scientific and engineering practices by Planning and carrying out investigations
• individually and collaboratively plan and conduct observational and experimental investigations 
• plan and conduct investigations or test design solutions in a safe manner, including planning for response to emergency situations 
• select and use appropriate tools and technology to collect, record, analyze, and evaluate data

CH.1.b

Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or ...

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two ...

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research ...

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not ...

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are ...

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally ...

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in ...

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more ...

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected ...

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control ...

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and ...

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is ...

How Data are Classified: Categorical Data

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How Data are Classified: Numerical Data

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Ogive Graph

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Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data ...

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. ...

What is a Mode?

The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one ...

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a ...

Median

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Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct ...

Midrange

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Bar Graph

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What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of ...

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can ...

Range

The range is one of the measures of variation. It can be defined as the difference between a dataset&#39;s highest and lowest values. For example, in the ...

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the ...

Standard Deviation

The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The ...

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each ...

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is ...

What is Central Tendency?

Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large ...

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. ...

Arithmetic Mean

The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the ...

Mean From a Frequency Distribution

Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the ...

The student will demonstrate an understanding of scientific and engineering practices by Interpreting, analyzing and evaluating data
• record and present data in an organized format that communicates relationships and quantities in appropriate mathematical or algebraic forms 
• use data in building and revising models, supporting explanations for phenomena, or testing solutions to problems 
• solve problems using mathematical manipulations including the International System of Units (SI), scientific notation, derived units, significant digits, and dimensional analysis 
• analyze data using tools, technologies, and/or models (e.g., computational, mathematical) to make valid and reliable scientific claims or determine an optimal design solution 
• analyze data graphically and use graphs to make predictions 
• differentiate between accuracy and precision of measurements
• consider limitations of data analysis when analyzing and interpreting data 
• analyze data to optimize a design

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about ...

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed&#160; the null hypothesis and the alternative hypothesis. These ...

critical-region-critical-values-and-significance-level

Critical Region, Critical Values and Significance Level

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In hypothesis testing, a sample ...

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value. &#160;P-value is the probability that, if the null ...

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed ...

The student will demonstrate an understanding of scientific and engineering practices by constructing and critiquing conclusions and explanations
• construct and revise explanations based on valid and reliable evidence obtained from a variety of sources 
• apply scientific ideas, principles, and/or evidence to provide an explanation of phenomena or design solutions 
• compare and evaluate competing arguments in light of currently accepted explanations and new scientific evidence 
• construct arguments or counterarguments based on data and evidence 
• differentiate between scientific hypothesis, theory, and law

CH.1.d

Dimensional Analysis

Dimensional analysis helps simplify fluid flow problems by using dimensionless groups, reducing complex variables into simpler terms that are easier to ...

Modeling and Similitude

A scaled model of a dam is tested in a controlled environment to observe how it will manage water flow, turbulence, and pressure.
This setup predicts the ...

Typical Model Studies

Typical model studies analyze scaled-down systems to predict fluid behavior, focusing on interactions with free surfaces and flow around immersed bodies.
...

The student will demonstrate an understanding of scientific and engineering practices by developing and using models
• evaluate the merits and limitations of models 
• develop, revise, and/or use models based on evidence to illustrate or predict relationships 
• use models and simulations to visualize and explain the movement of particles, to represent chemical reactions, to formulate mathematical equations, and to interpret data sets

CH.1.e

Random Assignment in Experiments

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