Name: biased sampling and fair tests
Uploaded: 2023-04-30T12:48:08.000Z
Duration: 1 min 22 s
Channel: JoVE

K12 Standards

Virginia  Standards of Learning

HIgh School

Biology

BIO.1

BIO.1.c

Bias

Los sesgos en los estudios de investigación son errores sistemáticos que favorecen u oponen una hipótesis de investigación.
El sesgo puede ocurrir intencionalmente o no durante la recopilación, análisis, interpretación o publicación de datos.
De los diversos tipos de sesgo, aquí se incluyen algunos comunes.
El sesgo de muestreo puede ocurrir cuando las muestras se extraen de forma no aleatoria de la población, que no es un representante ideal de toda la población, por ejemplo, predecir el resultado de una elección en función de las respuestas de una encuesta recopiladas solo de los miembros de un partido político y no de todo el electorado. 
El sesgo del observador o el sesgo de investigación puede ocurrir cuando las nociones preconcebidas, las expectativas o el conocimiento incompleto de un investigador influyen en los resultados y su interpretación.
El sesgo de medición se produce cuando se utilizan instrumentos de medición mal calibrados en el experimento.
El sesgo de publicación se observa cuando los estudios de investigación que informan hallazgos positivos estadísticamente significativos tienen más probabilidades de publicarse que los que informan resultados negativos.
En el caso de sesgo de financiamiento, los investigadores pueden sesgar los datos para mostrar resultados que favorezcan al organismo de financiamiento.

sesgo

El sesgo se refiere a cualquier tendencia que impide considerar una pregunta sin prejuicios. En la investigación, el sesgo ocurre cuando se selecciona o fomenta un resultado o respuesta sobre otros en el muestreo o las pruebas. El sesgo puede ocurrir durante cualquier fase de la investigación, incluido el diseño del estudio, la recopilación, el análisis y la publicación de datos.

En estadística, se crea un sesgo de muestreo cuando se recolecta una muestra de una población y algunos miembros de la población no tienen tantas probabilidades de ser elegidos como otros (recuerde, cada miembro de la población debe tener la misma probabilidad de ser elegido). Cuando ocurre un sesgo de muestreo, se pueden extraer conclusiones incorrectas sobre la población que se está estudiando. Además del sesgo de selección, se observan comúnmente varios tipos de sesgos en el diseño de experimentos y el análisis de datos: sesgo del observador, sesgo de medición, sesgo de publicación, etc.

Es importante darse cuenta de que en muchas situaciones los resultados no son igualmente probables. Una moneda o un dado pueden ser injustos o parciales. Dos profesores de matemáticas en Europa hicieron que sus estudiantes de estadística probaran la moneda belga de un euro y descubrieron que en 250 pruebas, se obtuvo cara el 56% de las veces y cruz el 44% de las veces. Los datos parecen mostrar que la moneda no es una moneda justa; sería útil realizar más repeticiones para sacar una conclusión más precisa sobre dicho sesgo. Algunos dados pueden estar sesgados. Mira los dados de algún juego que tengas en casa; las puntos en cada cara suelen ser pequeños agujeros tallados y luego pintados para hacerlas visibles. Tus dados pueden estar sesgados o no; es posible que los resultados se vean afectados por ligeras diferencias de peso debido al diferente número de agujeros en las caras. Los casinos de juego ganan mucho dinero dependiendo de los resultados de tirar los dados, por lo que los dados de los casinos se fabrican de manera diferente para eliminar el sesgo. Los dados de casino tienen caras planas; los agujeros se rellenan completamente con pintura que tiene la misma densidad que el material del que están hechos los dados, de modo que cada cara tiene la misma probabilidad de ocurrir.

Este texto es una adaptación de <a href="https://openstax.org/books/introductory-statistics/pages/3-introduction">Openstax, Introductory Statistics, Section 3, Probability. </a>

Sesgo

El sesgo se refiere a cualquier tendencia que impida que una pregunta se considere sin prejuicios. En la investigación, el sesgo se produce cuando se selecciona un resultado o una respuesta...

Education

Educación

JoVE Core

Statistics

Estadísticas

Unit 1

Unidad 1

Statistics in Practice

Estadística en la práctica

KEY TERMS AND CONCEPTS

TÉRMINOS Y CONCEPTOS CLAVE

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 chance of selection. If sampling bias occurs, researchers can draw incorrect conclusions about the population being studied.

Bias also appears in experiment design and data analysis. Observer bias, measurement bias, and publication bias are common examples. These problems can affect how results are seen, recorded, or shared.

Bias is not always a research problem. In statistics and probability, some outcomes are simply not equally likely. A coin or die may be unfair, or biased. Two math professors in Europe had statistics students test the Belgian one Euro coin, and in 250 trials heads appeared 56% of the time while tails appeared 44% of the time. That result suggests the coin may not be fair, although more trials would help make the conclusion stronger.

Dice can also be biased. Many game dice have spots made as small holes that are carved out and painted. Those small differences may slightly change the weight of each face. Casino dice are made differently to reduce bias. They have flat faces, and the holes are completely filled with paint that has the same density as the dice material so each face is equally likely to land.

Biased Sampling and Fair Tests

what is an experiment?

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...

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

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 bias affects sampling, experiments, and data analysis, plus why unfair coins and dice can change probability results in statistics.

Biased Sampling and Fair Tests | JoVE K12

statistics

JoVE Core Estadística presenta a los estudiantes los conceptos y las aplicaciones de la recopilación, análisis, presentación e interpretación de datos. Estas lecciones animadas proporcionan un entendimiento de la estadística a estudiantes de una amplia variedad de disciplinas. Además, los vídeos de científicos en acción muestran la aplicación de los conceptos en experimentos de investigación clásicos y originales, llevados a cabo hoy en día en laboratorios de ámbito internacional.

Chemistry

CH.1

CH.1.c

Physical Science

PS.1

PS.1.b

PS.1.f

Physics

PH.1

PH.1.b

PH.1.f

Texas Essential Knowledge and Skills

High School

CHEM.1

CHEM.1.B

Specialised Topic in Science

112.51.c.1.

112.51.c.2.B.

Georgia Standards of Excellence

9-12

Epidemiology

SEPI3.

SEPI3.c

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

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A ...

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 and/or
organisms, from examining models and theories, and/or to seek additional
information
• determine which questions can be investigated within the scope of the
school laboratory or field to determine relationships between independent
and dependent variables
• generate hypotheses based on research and scientific principles
• make hypotheses that specify what happens to a dependent variable when
an independent variable is manipulated

BIO.1.a

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological ...

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, ...

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be ...

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer ...

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals ...

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people&#39;s dietary ...

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except ...

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 and ethical manner including considerations of environmental, social, and personal effects
• determine appropriate sample size and techniques 
 • select and use appropriate tools and technology to collect, record, analyze, and evaluate data

BIO.1.b

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

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. ...

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data ...

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, ...

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. ...

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 ...

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 ...

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

Median

Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured ...

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 ...

Midrange

A somewhat easy to compute quantitative estimate of a data set&#8217;s central tendency is its midrange, which is defined as the mean of the minimum and ...

Clinical Trials

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

What is Variation?

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

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 ...

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 ...

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 ...

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. ...

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 ...

The student will demonstrate an understanding of scientific and engineering practices by interpreting, analyzing, and evaluating data 
 • construct and interpret data tables showing independent and dependent variables, repeated trials, and means
 • construct, analyze, and interpret graphical displays of data
 • use data in building and revising models, supporting an explanation for phenomena, or testing solutions to problems
 • analyze data using tools, technologies, and/or models to make valid and reliable scientific claims or determine an optimal design solution

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

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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
• make quantitative and/or qualitative claims regarding the relationship between dependent and independent variables 
• construct and revise explanations based on valid and reliable evidence obtained from a variety of sources including students' own investigations, models, theories, simulations, and peer review 
• apply scientific ideas, principles, and/or evidence to provide an explanation of phenomena and design solutions 
• compare and evaluate competing arguments or design solutions in light of currently accepted explanations and new scientific evidence
• construct arguments or counterarguments based on data and evidence differentiate between a scientific hypothesis and theory

BIO.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 
• develop and/or use models to generate data to support explanations, predict phenomena, analyze systems, and/or solve problems

BIO.1.e

Communication Process

The communication process is the transmission and reception of messages or information from one sender to the receiver.
It involves the sender encoding an ...

Communication Objectives

Marketing communication objectives refer to the strategic goals that guide promotional activities.
They set the stage for messaging strategies, content ...

The student will demonstrate an understanding of scientific and engineering practices by  obtaining, evaluating, and communicating information 
• compare, integrate, and evaluate sources of information presented in different media or formats to address a scientific question or solve a problem
• gather, read, and evaluate scientific and/or technical information from multiple authoritative sources, assessing the evidence and credibility of each source
• communicate scientific and/or technical information about phenomena in multiple formats

BIO.1.f

Biased Sampling and Fair Tests

Description

Transcript

Tags

More videos by Standards