StudySmarter - The all-in-one study app.

4.8 • +11k Ratings

More than 3 Million Downloads

Free

Suggested languages for you:

Americas

Europe

Bias in Experiments

- Calculus
- Absolute Maxima and Minima
- Absolute and Conditional Convergence
- Accumulation Function
- Accumulation Problems
- Algebraic Functions
- Alternating Series
- Antiderivatives
- Application of Derivatives
- Approximating Areas
- Arc Length of a Curve
- Area Between Two Curves
- Arithmetic Series
- Average Value of a Function
- Calculus of Parametric Curves
- Candidate Test
- Combining Differentiation Rules
- Combining Functions
- Continuity
- Continuity Over an Interval
- Convergence Tests
- Cost and Revenue
- Density and Center of Mass
- Derivative Functions
- Derivative of Exponential Function
- Derivative of Inverse Function
- Derivative of Logarithmic Functions
- Derivative of Trigonometric Functions
- Derivatives
- Derivatives and Continuity
- Derivatives and the Shape of a Graph
- Derivatives of Inverse Trigonometric Functions
- Derivatives of Polar Functions
- Derivatives of Sec, Csc and Cot
- Derivatives of Sin, Cos and Tan
- Determining Volumes by Slicing
- Direction Fields
- Disk Method
- Divergence Test
- Eliminating the Parameter
- Euler's Method
- Evaluating a Definite Integral
- Evaluation Theorem
- Exponential Functions
- Finding Limits
- Finding Limits of Specific Functions
- First Derivative Test
- Function Transformations
- General Solution of Differential Equation
- Geometric Series
- Growth Rate of Functions
- Higher-Order Derivatives
- Hydrostatic Pressure
- Hyperbolic Functions
- Implicit Differentiation Tangent Line
- Implicit Relations
- Improper Integrals
- Indefinite Integral
- Indeterminate Forms
- Initial Value Problem Differential Equations
- Integral Test
- Integrals of Exponential Functions
- Integrals of Motion
- Integrating Even and Odd Functions
- Integration Formula
- Integration Tables
- Integration Using Long Division
- Integration of Logarithmic Functions
- Integration using Inverse Trigonometric Functions
- Intermediate Value Theorem
- Inverse Trigonometric Functions
- Jump Discontinuity
- Lagrange Error Bound
- Limit Laws
- Limit of Vector Valued Function
- Limit of a Sequence
- Limits
- Limits at Infinity
- Limits at Infinity and Asymptotes
- Limits of a Function
- Linear Approximations and Differentials
- Linear Differential Equation
- Linear Functions
- Logarithmic Differentiation
- Logarithmic Functions
- Logistic Differential Equation
- Maclaurin Series
- Manipulating Functions
- Maxima and Minima
- Maxima and Minima Problems
- Mean Value Theorem for Integrals
- Models for Population Growth
- Motion Along a Line
- Motion in Space
- Natural Logarithmic Function
- Net Change Theorem
- Newton's Method
- Nonhomogeneous Differential Equation
- One-Sided Limits
- Optimization Problems
- P Series
- Particle Model Motion
- Particular Solutions to Differential Equations
- Polar Coordinates
- Polar Coordinates Functions
- Polar Curves
- Population Change
- Power Series
- Radius of Convergence
- Ratio Test
- Removable Discontinuity
- Riemann Sum
- Rolle's Theorem
- Root Test
- Second Derivative Test
- Separable Equations
- Separation of Variables
- Simpson's Rule
- Solid of Revolution
- Solutions to Differential Equations
- Surface Area of Revolution
- Symmetry of Functions
- Tangent Lines
- Taylor Polynomials
- Taylor Series
- Techniques of Integration
- The Fundamental Theorem of Calculus
- The Mean Value Theorem
- The Power Rule
- The Squeeze Theorem
- The Trapezoidal Rule
- Theorems of Continuity
- Trigonometric Substitution
- Vector Valued Function
- Vectors in Calculus
- Vectors in Space
- Washer Method
- Decision Maths
- Geometry
- 2 Dimensional Figures
- 3 Dimensional Vectors
- 3-Dimensional Figures
- Altitude
- Angles in Circles
- Arc Measures
- Area and Volume
- Area of Circles
- Area of Circular Sector
- Area of Parallelograms
- Area of Plane Figures
- Area of Rectangles
- Area of Regular Polygons
- Area of Rhombus
- Area of Trapezoid
- Area of a Kite
- Composition
- Congruence Transformations
- Congruent Triangles
- Convexity in Polygons
- Coordinate Systems
- Dilations
- Distance and Midpoints
- Equation of Circles
- Equilateral Triangles
- Figures
- Fundamentals of Geometry
- Geometric Inequalities
- Geometric Mean
- Geometric Probability
- Glide Reflections
- HL ASA and AAS
- Identity Map
- Inscribed Angles
- Isometry
- Isosceles Triangles
- Law of Cosines
- Law of Sines
- Linear Measure and Precision
- Median
- Parallel Lines Theorem
- Parallelograms
- Perpendicular Bisector
- Plane Geometry
- Polygons
- Projections
- Properties of Chords
- Proportionality Theorems
- Pythagoras Theorem
- Rectangle
- Reflection in Geometry
- Regular Polygon
- Rhombuses
- Right Triangles
- Rotations
- SSS and SAS
- Segment Length
- Similarity
- Similarity Transformations
- Special quadrilaterals
- Squares
- Surface Area of Cone
- Surface Area of Cylinder
- Surface Area of Prism
- Surface Area of Sphere
- Surface Area of a Solid
- Surface of Pyramids
- Symmetry
- Translations
- Trapezoids
- Triangle Inequalities
- Triangles
- Using Similar Polygons
- Vector Addition
- Vector Product
- Volume of Cone
- Volume of Cylinder
- Volume of Pyramid
- Volume of Solid
- Volume of Sphere
- Volume of prisms
- Mechanics Maths
- Acceleration and Time
- Acceleration and Velocity
- Angular Speed
- Assumptions
- Calculus Kinematics
- Coefficient of Friction
- Connected Particles
- Conservation of Mechanical Energy
- Constant Acceleration
- Constant Acceleration Equations
- Converting Units
- Elastic Strings and Springs
- Force as a Vector
- Kinematics
- Newton's First Law
- Newton's Law of Gravitation
- Newton's Second Law
- Newton's Third Law
- Power
- Projectiles
- Pulleys
- Resolving Forces
- Statics and Dynamics
- Tension in Strings
- Variable Acceleration
- Work Done by a Constant Force
- Probability and Statistics
- Bar Graphs
- Basic Probability
- Charts and Diagrams
- Conditional Probabilities
- Continuous and Discrete Data
- Frequency, Frequency Tables and Levels of Measurement
- Independent Events Probability
- Line Graphs
- Mean Median and Mode
- Mutually Exclusive Probabilities
- Probability Rules
- Probability of Combined Events
- Quartiles and Interquartile Range
- Systematic Listing
- Pure Maths
- ASA Theorem
- Absolute Value Equations and Inequalities
- Addition and Subtraction of Rational Expressions
- Addition, Subtraction, Multiplication and Division
- Algebra
- Algebraic Fractions
- Algebraic Notation
- Algebraic Representation
- Analyzing Graphs of Polynomials
- Angle Measure
- Angles
- Angles in Polygons
- Approximation and Estimation
- Area and Circumference of a Circle
- Area and Perimeter of Quadrilaterals
- Area of Triangles
- Argand Diagram
- Arithmetic Sequences
- Average Rate of Change
- Bijective Functions
- Binomial Expansion
- Binomial Theorem
- Chain Rule
- Circle Theorems
- Circles
- Circles Maths
- Combination of Functions
- Combinatorics
- Common Factors
- Common Multiples
- Completing the Square
- Completing the Squares
- Complex Numbers
- Composite Functions
- Composition of Functions
- Compound Interest
- Compound Units
- Conic Sections
- Construction and Loci
- Converting Metrics
- Convexity and Concavity
- Coordinate Geometry
- Coordinates in Four Quadrants
- Cubic Function Graph
- Cubic Polynomial Graphs
- Data transformations
- De Moivre's Theorem
- Deductive Reasoning
- Definite Integrals
- Deriving Equations
- Determinant of Inverse Matrix
- Determinants
- Differential Equations
- Differentiation
- Differentiation Rules
- Differentiation from First Principles
- Differentiation of Hyperbolic Functions
- Direct and Inverse proportions
- Disjoint and Overlapping Events
- Disproof by Counterexample
- Distance from a Point to a Line
- Divisibility Tests
- Double Angle and Half Angle Formulas
- Drawing Conclusions from Examples
- Ellipse
- Equation of Line in 3D
- Equation of a Perpendicular Bisector
- Equation of a circle
- Equations
- Equations and Identities
- Equations and Inequalities
- Estimation in Real Life
- Euclidean Algorithm
- Evaluating and Graphing Polynomials
- Even Functions
- Exponential Form of Complex Numbers
- Exponential Rules
- Exponentials and Logarithms
- Expression Math
- Expressions and Formulas
- Faces Edges and Vertices
- Factorials
- Factoring Polynomials
- Factoring Quadratic Equations
- Factorising expressions
- Factors
- Finding Maxima and Minima Using Derivatives
- Finding Rational Zeros
- Finding the Area
- Forms of Quadratic Functions
- Fractional Powers
- Fractional Ratio
- Fractions
- Fractions and Decimals
- Fractions and Factors
- Fractions in Expressions and Equations
- Fractions, Decimals and Percentages
- Function Basics
- Functional Analysis
- Functions
- Fundamental Counting Principle
- Fundamental Theorem of Algebra
- Generating Terms of a Sequence
- Geometric Sequence
- Gradient and Intercept
- Graphical Representation
- Graphing Rational Functions
- Graphing Trigonometric Functions
- Graphs
- Graphs and Differentiation
- Graphs of Common Functions
- Graphs of Exponents and Logarithms
- Graphs of Trigonometric Functions
- Greatest Common Divisor
- Growth and Decay
- Growth of Functions
- Highest Common Factor
- Hyperbolas
- Imaginary Unit and Polar Bijection
- Implicit differentiation
- Inductive Reasoning
- Inequalities Maths
- Infinite geometric series
- Injective functions
- Instantaneous Rate of Change
- Integers
- Integrating Polynomials
- Integrating Trig Functions
- Integrating e^x and 1/x
- Integration
- Integration Using Partial Fractions
- Integration by Parts
- Integration by Substitution
- Integration of Hyperbolic Functions
- Interest
- Inverse Hyperbolic Functions
- Inverse Matrices
- Inverse and Joint Variation
- Inverse functions
- Iterative Methods
- Law of Cosines in Algebra
- Law of Sines in Algebra
- Laws of Logs
- Limits of Accuracy
- Linear Expressions
- Linear Systems
- Linear Transformations of Matrices
- Location of Roots
- Logarithm Base
- Logic
- Lower and Upper Bounds
- Lowest Common Denominator
- Lowest Common Multiple
- Math formula
- Matrices
- Matrix Addition and Subtraction
- Matrix Determinant
- Matrix Multiplication
- Metric and Imperial Units
- Misleading Graphs
- Mixed Expressions
- Modulus Functions
- Modulus and Phase
- Multiples of Pi
- Multiplication and Division of Fractions
- Multiplicative Relationship
- Multiplying and Dividing Rational Expressions
- Natural Logarithm
- Natural Numbers
- Notation
- Number
- Number Line
- Number Systems
- Numerical Methods
- Odd functions
- Open Sentences and Identities
- Operation with Complex Numbers
- Operations with Decimals
- Operations with Matrices
- Operations with Polynomials
- Order of Operations
- Parabola
- Parallel Lines
- Parametric Differentiation
- Parametric Equations
- Parametric Integration
- Partial Fractions
- Pascal's Triangle
- Percentage
- Percentage Increase and Decrease
- Percentage as fraction or decimals
- Perimeter of a Triangle
- Permutations and Combinations
- Perpendicular Lines
- Points Lines and Planes
- Polynomial Graphs
- Polynomials
- Powers Roots And Radicals
- Powers and Exponents
- Powers and Roots
- Prime Factorization
- Prime Numbers
- Problem-solving Models and Strategies
- Product Rule
- Proof
- Proof and Mathematical Induction
- Proof by Contradiction
- Proof by Deduction
- Proof by Exhaustion
- Proof by Induction
- Properties of Exponents
- Proportion
- Proving an Identity
- Pythagorean Identities
- Quadratic Equations
- Quadratic Function Graphs
- Quadratic Graphs
- Quadratic functions
- Quadrilaterals
- Quotient Rule
- Radians
- Radical Functions
- Rates of Change
- Ratio
- Ratio Fractions
- Rational Exponents
- Rational Expressions
- Rational Functions
- Rational Numbers and Fractions
- Ratios as Fractions
- Real Numbers
- Reciprocal Graphs
- Recurrence Relation
- Recursion and Special Sequences
- Remainder and Factor Theorems
- Representation of Complex Numbers
- Rewriting Formulas and Equations
- Roots of Complex Numbers
- Roots of Polynomials
- Roots of Unity
- Rounding
- SAS Theorem
- SSS Theorem
- Scalar Triple Product
- Scale Drawings and Maps
- Scale Factors
- Scientific Notation
- Second Order Recurrence Relation
- Sector of a Circle
- Segment of a Circle
- Sequences
- Sequences and Series
- Series Maths
- Sets Math
- Similar Triangles
- Similar and Congruent Shapes
- Simple Interest
- Simplifying Fractions
- Simplifying Radicals
- Simultaneous Equations
- Sine and Cosine Rules
- Small Angle Approximation
- Solving Linear Equations
- Solving Linear Systems
- Solving Quadratic Equations
- Solving Radical Inequalities
- Solving Rational Equations
- Solving Simultaneous Equations Using Matrices
- Solving Systems of Inequalities
- Solving Trigonometric Equations
- Solving and Graphing Quadratic Equations
- Solving and Graphing Quadratic Inequalities
- Special Products
- Standard Form
- Standard Integrals
- Standard Unit
- Straight Line Graphs
- Substraction and addition of fractions
- Sum and Difference of Angles Formulas
- Sum of Natural Numbers
- Surds
- Surjective functions
- Tables and Graphs
- Tangent of a Circle
- The Quadratic Formula and the Discriminant
- Transformations
- Transformations of Graphs
- Translations of Trigonometric Functions
- Triangle Rules
- Triangle trigonometry
- Trigonometric Functions
- Trigonometric Functions of General Angles
- Trigonometric Identities
- Trigonometric Ratios
- Trigonometry
- Turning Points
- Types of Functions
- Types of Numbers
- Types of Triangles
- Unit Circle
- Units
- Variables in Algebra
- Vectors
- Verifying Trigonometric Identities
- Writing Equations
- Writing Linear Equations
- Statistics
- Bias in Experiments
- Binomial Distribution
- Binomial Hypothesis Test
- Bivariate Data
- Box Plots
- Categorical Data
- Categorical Variables
- Central Limit Theorem
- Chi Square Test for Goodness of Fit
- Chi Square Test for Homogeneity
- Chi Square Test for Independence
- Chi-Square Distribution
- Combining Random Variables
- Comparing Data
- Comparing Two Means Hypothesis Testing
- Conditional Probability
- Conducting a Study
- Conducting a Survey
- Conducting an Experiment
- Confidence Interval for Population Mean
- Confidence Interval for Population Proportion
- Confidence Interval for Slope of Regression Line
- Confidence Interval for the Difference of Two Means
- Confidence Intervals
- Correlation Math
- Cumulative Distribution Function
- Cumulative Frequency
- Data Analysis
- Data Interpretation
- Degrees of Freedom
- Discrete Random Variable
- Distributions
- Dot Plot
- Empirical Rule
- Errors in Hypothesis Testing
- Estimator Bias
- Events (Probability)
- Frequency Polygons
- Generalization and Conclusions
- Geometric Distribution
- Histograms
- Hypothesis Test for Correlation
- Hypothesis Test for Regression Slope
- Hypothesis Test of Two Population Proportions
- Hypothesis Testing
- Inference for Distributions of Categorical Data
- Inferences in Statistics
- Large Data Set
- Least Squares Linear Regression
- Linear Interpolation
- Linear Regression
- Measures of Central Tendency
- Methods of Data Collection
- Normal Distribution
- Normal Distribution Hypothesis Test
- Normal Distribution Percentile
- Paired T-Test
- Point Estimation
- Probability
- Probability Calculations
- Probability Density Function
- Probability Distribution
- Probability Generating Function
- Quantitative Variables
- Quartiles
- Random Variables
- Randomized Block Design
- Residual Sum of Squares
- Residuals
- Sample Mean
- Sample Proportion
- Sampling
- Sampling Distribution
- Scatter Graphs
- Single Variable Data
- Skewness
- Spearman's Rank Correlation Coefficient
- Standard Deviation
- Standard Error
- Standard Normal Distribution
- Statistical Graphs
- Statistical Measures
- Stem and Leaf Graph
- Sum of Independent Random Variables
- Survey Bias
- T-distribution
- Transforming Random Variables
- Tree Diagram
- Two Categorical Variables
- Two Quantitative Variables
- Type I Error
- Type II Error
- Types of Data in Statistics
- Variance for Binomial Distribution
- Venn Diagrams

We've all experienced some form of bias in one way or the other. You may have seen it happen to others, experienced it yourself or even participated in it. Bias here means favoring something over another even when the thing being favored does not deserve to be.

Aside from our everyday lives, bias also occurs during experiments and research. In this article, you will learn about the sources, types and examples of bias in experiments.

Before going in-depth, let's see what bias in experiments means.

**Bias **in experiments refers to a known or unknown influence in the experimental process, data or results.

Bias can come from anywhere. It can be from the scientist conducting the experiment, the participants of the experiments or it may come from the way the experiment is being conducted. Before we go in-depth, let's learn about something called the placebo effect.

The placebo effect is used all the time especially in the medical sector.

A **placebo** is a medicine or procedure that has no active substance and no real effect.

It involves receiving a treatment that causes improvement (or possibly side effects) even when its fake. The placebo effect is used to test the effectiveness of a treatment and if the real treatment performs much better than the placebo, then you know it really works. The participants getting the placebo should think they are getting the real thing. Otherwise, the effect may not be felt.

The participants must be **blind **to which type of treatment they are getting. Since the participant of the treatment should not know what type they are getting, something has to be done to make sure that it is so.

**Blinding** means to keep information from someone about the type of treatment they are getting.

It is possible for the person administering the treatment to subconsciously give cues that may make the patient or participant know that something is wrong. For this reason, both the participant and the person administering the treatment should not know if its a placebo or not. This is called **double blinding**. When the patient or participant is the only one unaware of the type of treatment received, it is called **single blinding.**

When the placebo effect works, it doesn't mean that the illness was false. One thing that has happened is that the mind and body of the person is relaxed knowing that it is taking some kind of medication. Some symptom causing hormones may reduce as a result and the body begins to act the way it should without the illness.

That is why experiments use a **control group.**

A **control group **is a group that does not receive any treatment during an experiment.

For the placebo effect, one group receives the treatment while the other group receives an inactive treatment but here, one group receives the treatment but the other receives nothing at all.Let's take a look at the sources of bias in experiments.

As earlier stated, you have bias in experiments when the experimental process is knowingly or unknowingly influenced, affecting the outcome of the experiment. Bias can come from different sources. It can come from the scientist, the participants of the experiment or the experimental environment.

Below are some sources of bias in experiments.

The method of data collection and the source of the data can lead to bias in experiments. To learn about the methods of data collection, see the article on Methods of Data Collection.

Not considering all possible outcomes can lead to bias. Even though, it is not really possible to consider all outcomes, scientist should make an effort to perform more experiments to control any new source of bias found.

Unknown changes in the experimental environment can lead to bias.

False behavior and response from the participants can lead to bias.

Let's now see some types of bias in experiments.

The following are some types of bias.

- Participant or Selection Bias.
- Publication Bias.
- Confirmation Bias
- Observation Bias
- Confounding Bias.
- Design Bias.

Let's see what each of them are about.

**Participant bias** has to do with the population. It occurs when a certain group of people are selected to participate in an experiment or research. This group of people maybe of the same age, same gender or may have the same characteristics or behavior. The problem here is that only one category of the population is considered. The experiment will not cover the effect on the rest of the population.

For example, if you have a new vaccine that you want to test and you test it only on healthy people who are between the ages of 20 to 30 years old. The data you will get from this test cannot tell how effective the vaccine will be on the entire population. Your test does not give you information on the effect on people younger than 20, people older than 30 or people with underlying health conditions. The data from this experiment is not sufficient to release this vaccine to the public.

The way to avoid participant bias is to include various categories of people while conducting your experiment. You have to make sure that all possible beneficiaries of your experiment are investigated to know the effect on them.

**Publication bias** occurs when only the positive or interesting aspect of a scientific study is published. There are many reasons why this happens. One reason is because people are more likely to accept your findings or product when they feel it will do little or no harm to them.

This bias is seen a lot in the medical sector when a new drug or treatment method is being introduced. Sometimes, they want to down-play the negative effect of what they are proposing so it can be accepted. That is why in the US when you see an ad for a new drug it has to list all of the possible side effects for the drug.

Another reason for publication bias is the standard and criteria that has been set for the publication of research papers in a certain fields. Some of these criteria may require you to leave out some information or down-play some things. The authors of these papers make adjustments so that their papers can be published.

One other reason is that those conducting the experiment may want to favor those funding the experiment thereby omitting information, especially the negative ones that may harm those funding.

Publication bias leads to limited information and understanding of a particular topic. It can also negatively affect the health and quality of living of the public.

**Confirmation bias** occurs when you are carrying out an experiment for the purpose of confirming your hypothesis. The problem here is that you would want your hypothesis to be true. So, you unconsciously follow procedures and seek information that will confirm your hypothesis. You ignore everything that will say otherwise. This can lead to wrong conclusion.

You avoid this by considering all options during your experimental process and keep the possibility of your hypothesis being wrong in mind.

**Observation bias** is seen in experiments where scientist observe the behavior of the participants. Sometimes, the participants knowingly or unknowingly act or behave in ways they would normally not behave because they know that they are being watched. Their false behavior will lead to incorrect conclusion of the experiment.

**Confounding bias** is a type of bias that is as a result of an external factor affecting the relationship or association between a variable or subject that is being studied and its outcome. This external factor is called a confounder. The presence of the confounder affects the accuracy of the outcome.

**Design bias** affects the outcome or conclusion of the experiment. This happens as a result of the methods and the procedures you follow while conducting the experiment. To avoid design bias, the scientist need to keep in mind all other possible bias that can occur during the experiment process and try to avoid them.

Avoiding bias is often called **controlling for sources of bias**. The following are some ways in which you can avoid bias in experiments.

Ensure that the participants in your experiment represent all categories that are likely to benefit from the experiment.

Ensure that no important findings from your experiments are left out.

Consider all possible outcomes while conducting your experiment.

Make sure your methods and procedures are clean and correct.

Seek the opinions of other scientists and allow them review you experiment. They maybe able to identify things you have missed.

Collect data from multiple sources.

Allow participants to review the conclusion of your experiment so they can confirm that the conclusion accurately represents what they portrayed.

The hypothesis of an experiment should be hidden from the participants so they don't act in favor or maybe against it.

Let's see some advantages of eliminating bias in experiments.

- The results and conclusion of the experiment will be reliable and dependable.
- There will be better chances of the experiment helping as much people as it should.
- Important information and findings will not be hidden or left out.
- The conclusion of the experiment will not be influenced by any specific opinion.
- The scientist will be open minded and consider all possibilities while conducting the experiment.
- The data collected will be more accurate.
- Detailed and complete articles and journals for the experiment will be published.

Let's take a look at some practical examples of bias in science experiments.

You have an hypothesis that artificial coloring of food causes hyperactivity in children. To investigate this, you take two groups of children and give one group fruits and the other group artificial colored sweets. The group of children that ate the artificial colored sweets were hyperactive which confirms the hypothesis.

What kind of bias can you identify in this experiment and explain why it is a bias?

**Solution:**

The type of bias here is confirmation bias. You have not considered that those group of children were hyperactive because of the sugar they were consuming, or the fact that they haven't been getting much exercise, and not because of the artificial coloring.

Let's see another example.

You are conducting an experiment to see the effect of a particular supplement in young males. Over 60% of the participants are African Americans and the rest are Caucasians.

What kind of bias can you identify for this experiment and explain why it is a bias?

**Solution:**

The bias here is participant or selection bias. With your participants, there is under representation and over representation of two groups of people and you have not even considered other races. Unless your research is exclusive to a particular race, your participants have to be diverse.

Let's see another example.

For the purpose of meeting some publication criteria or guidelines, you have decided to omit some useful information from your research.

What type of bias is this?

**Solution:**

This type of bias is called publication bias.

Let's look at one more example.

You are trying to study the behavior of a group of people. The participants of the experiment are aware of the experiment hypothesis and they also know they are being watched. Because of this, they try to act in ways that they feel is acceptable.

What kind of bias can you identify here?

**Solution:**

This type of bias is called observation bias. The hypothesis of an experiment should be hidden from the participants so they don't act in favor or against it.

- Bias in experiments refers to a known or unknown influence in the experimental process, data or results.
- Some types of bias are below.
- Participant or Selection Bias.
- Publication Bias.
- Confirmation Bias
- Observation Bias
- Confounding Bias.
- Design Bias.

- Bias can come from anywhere. It can be from the scientist conducting the experiment, the participants of the experiments or it may come from the way the experiment is being conducted.

The following are some ways in which you can avoid bias in experiments.

- Ensure that the participants in your experiment represents represent all categories that are likely to benefit from the experiment.
- Ensure that no important findings from your experiments are left out.
- Consider all possible outcomes while conducting your experiment.
- Make sure your methods and procedures are clean and correct.
- Seek the opinions of other scientists and allow them review you experiment. They maybe able to identify things you have missed.
- Collect data from multiple sources.
- Allow participants to review the conclusion of your experiment so they can confirm that the conclusion accurately represents what they portrayed.
- The hypothesis of an experiment should be hidden from the participants so they don't act in favor or maybe against it.

The following are some ways to prevent bias in experiments.

- Seek the opinions of other scientists and allow them review you experiment. They maybe able to identify things you have missed.
- Collect data from multiple sources.
- Allow participants to review the conclusion of your experiment so they can confirm that the conclusion accurately represents what they portrayed.
- The hypothesis of an experiment should be hidden from the participants so they don't act in favor or maybe against it.

More about Bias in Experiments

Be perfectly prepared on time with an individual plan.

Test your knowledge with gamified quizzes.

Create and find flashcards in record time.

Create beautiful notes faster than ever before.

Have all your study materials in one place.

Upload unlimited documents and save them online.

Identify your study strength and weaknesses.

Set individual study goals and earn points reaching them.

Stop procrastinating with our study reminders.

Earn points, unlock badges and level up while studying.

Create flashcards in notes completely automatically.

Create the most beautiful study materials using our templates.

Sign up to highlight and take notes. It’s 100% free.

Over 10 million students from across the world are already learning smarter.

Get Started for Free