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What is Hybrid Securities?

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The two most popularly known categories of securities are equities (variable income securities)and bonds (fixed income securities). The purchase of equities offers partial ownership of a company, while bonds mean lending money to a company and receiving interest as a form of fixed income. Now, hybrid securities are a combination of both security types (equities and bonds), and their return is based on the performance of the underlying assets.


They usually offer a higher rate of return than pure fixed-income securities, but the return is lower than pure variable-income securities.

They believe that hybrid securities are less risky than pure variable-income securities but riskier than pure fixed-income securities.

Investors can use these securities to participate in a company’s capital raising program without having either the risk of a stock or less liquidity of a bond.

Types of Hybrid Securities

Preferred Stocks: In this type of hybrid securities, investors enjoy seniority to common stockholders and, as such, receive dividends before them. The quantum of tips for preferred stocks usually differs from common stockholders. In case of insolvency, the businesses are obligated to repay the preferred stockholders before the common stockholders. In addition, companies sometimes provide preferred stockholders the option to convert their holdings into common stocks.

In-kind toggle Notes: This type helps companies that are undergoing a liquidity crunch in raising capital to fund the short-term liquidity gap. In such securities, the issuing company can capitalize on the interest payment that results in additional debt. In-kind toggle notes come with the provision to delay the interest payment during serious liquidity shortfall.

Convertible Bonds: This type is a fixed-income instrument with a call option on some equity. Effectively, the investors of these securities can convert their holdings into a predetermined number of company stocks. In fact, in some cases, the investors also have the option to convert the fixed income instrument of one company into a fixed number of stores of some other company. Convertible bonds generally offer a lower rate of return compared to traditional fixed-income bonds, and the interest rate differential represents the premium that convertible bondholders pay for the call option.

Risks of Hybrid Securities

Some of the significant risks associated with hybrid securities are as follows:

There is a huge liquidity risk in such securities as the trading volumes can vary significantly, driven by their demand and supply in the market.

The volatility in market price exposes the expected return to unpredictability, making it exceptionally unpredictable.

If the issuing company experiences a loss of earnings, other senior debt obligations can significantly diminish the interest payments on these securities.

The return on these securities is also subject to the risk of regulatory changes or changes in tax laws.


It typically provides a relatively higher return compared to other senior obligations because they are often subordinate in the capital structure.

Hybrid securities exhibit relatively lower volatility than the overall market as they offer stable and fixed distribution of market returns.

Since hybrid securities are not required to adhere to equities or bonds rigidly, they might have a diversified risk profile.

Investing in hybrid securities is regarded as more complex compared to investments in either equities or bonds.

Due to its subordinated position to other senior obligations, hybrid securities can experience substantial losses in the event of the issuing company’s bankruptcy.

Essential Terms About Hybrid Securities

Reset Date: It refers to the date on which the hybrid securities’ terms and conditions (interest rate, next reset date, etc.) may change.

Cumulative Dividend: In this system, if the issuing company cannot pay the dividend in a particular period, it gets added to the next dividend payment.

Non-Cumulative Dividend: If the issuing company cannot pay the dividend in a particular period, it is waived off.

Redeemable Securities: In this case, the holder of the securities has the option to sell it back to the issuing company at the issue price.

Non-Redeemable Securities: In this case, the holder of the guards doesn’t have the option to sell back.

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A Broader Understanding Of Ml And Types Of Regression


Machine Learning(ML) is the science of interpreting the data and automating the functioning of data analytics by training it through the existing data so that it makes a decision when a similar type of data is passed to it. ML comes under AI(Artificial intelligence) umbrella and many others come under AI where there is a creation of intelligent systems. Below is a small diagram I made for representing AI, ML, and Data Science.

Today, examples of machine learning are all around us. Digital assistants (like Siri, Alexa, Google) work in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before is how Recommendation systems came up. Robots vacuum our floors while we do other things. I think people can find these robots to buy at e-commerce stores as home assistants. How the technology evolved for our service?

ML Classification:

Machine Learning is classified into four types. They are Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning. Supervised Learning deals with the model being trained with labeled data and making predictions on it. It is easy to evaluate these models as the true values(correct values) are already known. Unsupervised Learning deals with unlabelled datasets where there are no labels given to them or which cannot be given to them.

These models identify the unidentified patterns in it to make predictions. Semi-supervised is the combination of both supervised and unsupervised where some of the data contain labels and others don’t. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Reinforcement Learning is like trial and error methods where the correct predictions are rewarded and the incorrect predictions model is penalized and retrained for actions.

ML is mostly used on datasets where Supervised and Unsupervised are the common techniques used for getting the information from it. The other types are also used then firstly the first two classifications are to be known which helps in understanding the latter types well.

Supervised Learning is further classified into two types. They are Regression and Classification tasks. Regression is used for predicting the continuous values and Classification in predicting the discrete values. More clearly, Regression is used for numerical value prediction and Classification for categorical data prediction. Eg: Regression is used for the prediction of house prices, Stock prices, fare prediction, ..etc.Classification is used for the prediction of medical diagnosis(disease is present or not), fraud detection(fraud or no fraud), Rain forecast(Rain or no rain), etc.

There are many models for solving Regression problems like Linear Regression, KNN, SVR, Decision Tree, Random Forest, Boosting methods, and many more.

Predictive Analytics and various other types of analytics:

There are four types of analytics. They are Descriptive, Diagnostic, Predictive, and Prescriptive analytics. ML algorithms come under Predicting modeling techniques. Below is the pictorial representation I have made for understanding the types of analytics.

Regression analysis to deal with Regression problems in ML and for various other findings in the data:

Regression analysis is used for knowing the relationship between the independent(predictor variables) and dependent(predicted) variables. This technique is used for forecasting, time series modeling, and finding the causal effect relationship between the variables.

Some of the regression techniques used for analysis are Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic net Regression, and Stepwise regression.

1. Linear Regression

This is also known as OLS(Ordinary least squares)method or Linear least squares method. The linear regression is represented by the equation y= a + bx + e where x = independent variable, a = intercept, b = slope of the line and e = error term. The simple linear regression contains one independent variable whereas multiple linear regression contains more than one independent variable then the equation turns out be y = b0 + b1x1 + b2x2 + …bnxn + e where b0 = intercept; b1,b2, = slope of the line, x1,x2,x3…xn = independent variables or features, e = error term.

The assumptions of Linear regression are:

Linearity: The relationship between X and Y is linear.

Homoscedasticity: The variance of residual is the same for any value of X.

Independence: Observations are independent of each other. No correlation between error terms. The absence of this phenomenon is Autocorrelation.

No Multicollinearity: There should not be multicollinearity in the regression model. Multicollinearity occurs when there is a high correlation between two independent variables.

Normality: For any fixed value of X, Y is normally distributed. The error terms must be normally distributed.

The best fit line is constructed like the above based on all the data points where the loss function is minimum or the error is less that becomes the best fit line for the data. Gradient descent algorithm gives different weights to the equation and using different learning rates whichever gets the minima/maxima cost function becomes the parameters for the linear equation y = a + bx + e. The cost function used to minimize the linear equation error rate is the mean squared error. The equation for it is given below:

When you have got the above stats implementation is just a few code blocks away.

from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score model=LinearRegression(),y_train) pred=model.predict(x_test) r2_score(y_test,pred) #for finding r2 score of the predicted and true values

You can explore Linear Regression parameters in the Linear Regression sklearn library

2. Lasso Regression

This is also known as L1 Regression. Lasso is the short form for the Least absolute shrinkage and selection operator. Lasso penalizes the absolute size of the regression coefficients. Regularized linear regression models are very similar to least squares followed in Linear Regression, except that the coefficients are estimated by minimizing a slightly different objective function. We minimize the sum of RSS and a “penalty term” that penalizes coefficient size. The larger the penalty applied, the further the estimates or values get shrunk towards absolute zero. This results in variable selection out of given variables. The equation to find the loss function for L1 is given below:

3. Ridge Regression

This is also known as L2 Regression. Ridge regression acts as a remedial measure used to ease collinearity in between predictors of a model, since, the model includes correlated featured variables, so the final model is confined and rigid in its maximum approach. L2 eliminates the problem of the data facing multicollinearity between the variables.

4. Elastic Net Regression or Regularisation

This is the combination of both L1 and L2 regularization to get the benefits of both methods. Now on using this error decreases a lot compared to using one of it. It uses both Lasso as well as Ridge Regression regularization to remove all unnecessary coefficients but not the informative ones. The below is the equation to calculate it;

5. Polynomial Regression

The linear regression works well only with linear data whereas polynomial regression is used for non-linear or when the data points are in the form of a curve. When executing a model that is fit to manage non-linearly separated data, the polynomial regression technique is used. In it, the best-fitted line is not straight, instead, a curve that best-fitted to data points.

The equation for polynomial equation comes in the form of degrees based on the variables available or the number of features to be fit in polynomial regression. The equation for it is y = b0 + b1x1 + b2x2^2 + b3x3^3 +……+ bnxn^n, where b0 = intercept, b1,b2, = intercept of the polynomial regression curve, x1,x2^2…..xn^n = the n square of xn variables represent the independent variables squared up to the degree of the polynomial.

6. Stepwise Regression

In this method, regression is constructed until and unless all the variables are in the required p-value or t-stats or test statistic. With every forward step, the variable gets added or subtracted from a group of descriptive variables.

The criteria followed in the stepwise regression technique are forward determination (forward selection), backward exclusion (backward elimination), and bidirectional removal (bidirectional elimination). This is achieved by observing statistical values like R-square, t-stats, and AIC metric to discern significant variables. Stepwise regression fits the regression model by adding/dropping co-variates one at a time based on a specified criterion.

There are many ways to do feature selection instead of Univariate Feature selection using stats(p-value,f test, chi-square).UFS selects features based on univariate statistical tests, which evaluate the relationship between two randomly selected variables. In the case of a continuous dependent variable, two options are available: f-regression and mutual_info_regression.

Several options are available but two different ways of specifying the removal of features are (a) SelectKBest removes all low scoring features, and (b) SelectPercentile allows the analyst to specify a scoring percent of features, and all features not reaching that threshold then are removed. To do this different methods like OLS, RFE(Recursive feature elimination), RFECV(Recursive feature elimination and cross-validation), SelectKBest, GenericUnivariateSelect. Feature selection using SelectFromModel allows the analyst to make use of L1-based feature selection (e.g. Lasso) and tree-based feature selection. All the above methods are available in sklearn.

This is a summary made for understanding Machine Learning and some of the statistics linked with Regression models. Let me know if there are any questions and difficulties in it or any other related queries. Thanks for reading.😎 Have a nice day.😊

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Different Types Of Lip Products And Their Uses

Lip products have become a staple in many people’s makeup routines, offering a variety of options to enhance and protect the lips. From lip balms to lipsticks, glosses to stains, there are countless types of lip products available in the market today. Each product serves a unique purpose, from providing hydration and nourishment to adding color and shine. With so many options to choose from, it’s easy to find a lip product that suits your individual needs and preferences.

Lips Products

One of your face’s most appealing traits is the shape of your lips. They might give you a fashionable or even boring appearance. There are numerous lip cosmetic options on the market that can transform your appearance. Moreover, lip makeup is important for face makeup. Hence, it would be beneficial if you carefully selected the lip tints to make your lips look fantastic. The list below might help you learn more about the various lip creams and how they can affect your lips.

Lip Oil

Lip oils can be worn alone or on top of lipstick for extra shine and hydration. They are available in a variety of colours and finishes, from clear to tinted. They are a fantastic option for anyone who wish to maintain soft, smooth lips while achieving a natural, effortless look.

Lip Gloss

A classic lip cosmetic, lip gloss gives the lips a glossy, high-shine look. Typically, oils, waxes, and polymers are included in its formulation to assist create a smooth, shiny finish. Lip glosses are available in a wide range of colours and formulations, from glittery to metallic to holographic, and come in a number of textures, including transparent, sheer, and opaque. They can be used on their own or over lipstick for more dimension and shine. For individuals who want to add a dash of glitz to their makeup look or highlight the natural beauty of their lips, lip glosses are a fantastic alternative.

Lip Balm

The purpose of lip balm, a type of lip lotion, is to deeply hydrate and nourish the lips. Beeswax, shea butter, and coconut oil are frequently used in its formulation since they assist to calm and protect the delicate skin on the lips. Lip balms are offered in a number of flavours and scents and come in a range of forms, including classic sticks, pots, and tubes. They offer long-lasting hydration and aid to halt further drying or cracking, making them a fantastic option for people with dry or chapped lips. Lip balms can be used to add moisture and smoothness on their own or under lipstick.

Lip Plumper

A lip product called a lip plumper is made to increase the volume and fullness of the lips. It is frequently made with chemicals like cinnamon or peppermint oil, which temporarily boost blood flow to the area and give the lips a plumper appearance by stimulating the lips. Hyaluronic acid and collagen are additional ingredients in certain lip plumper’s, which aid to moisturize and plump the lips over time. Lip plumper’s can be worn alone or over lipstick to give lips more fullness and dimension. They are available in a variety of forms, from clear to coloured. They are a fantastic option for people who want a pout that is fuller and more voluminous.

Exfoliating Lip Products

Lip products with exfoliating properties are made to exfoliate dead skin cells from the lips and expose softer, smoother skin. They are frequently made with fruit enzymes, sugar, salt, or other exfoliating chemicals that assist to gently remove dry, flaky skin. Exfoliating lip products are offered in a number of flavours and smells and come in a range of compositions, including scrubs, masks, and balms. They offer a gentle and efficient technique to remove dead skin and support healthy, moisturized lips, making them a fantastic option for people who have dry or chapped lips.


The traditional lip cosmetic, lipstick, gives the lips long-lasting colour and definition. It is often made with pigments, oils, and waxes, which contribute to the creation of a creamy, smooth texture that applies effortlessly and maintains its appearance for hours. Lipsticks are offered in a number of colours and finishes, with a range of formulas including matte, satin, and glossy. For more depth and complexity, layer them with other lip treatments like lip liner or gloss or wear them alone. Lipsticks are a fantastic option for people who want to add a burst of colour to their makeup or make a big statement with their lip colour.

Whipped Lip Butter

A sort of lip balm that offers the lips exceptional moisture and nourishment is whipped lip butter. Shea butter, cocoa butter, and coconut oil are common hydrating components included in its formulation, all of which assist to soothe and soften dry, chapped lips. Lips feel soft and silky after applying whipped lip butter, which frequently contains natural flavours or aromas for an enticing, aromatic experience. It offers a glossy, translucent finish that highlights the lips’ inherent beauty and offers long-lasting moisture and defense. When looking for a high-end, hydrating lip product that feels rich and nurturing, whipped lip butter is a fantastic option.


Knowledge of the various lip product varieties and their applications is crucial for attaining the desired look and preserving healthy lips. There are numerous solutions to fit your needs, whether you choose a natural or strong lip look. You may choose the ideal lip product to improve your overall appearance and keep your lips feeling and looking their best by taking into account elements like texture, colour, and formulation.

A wide range of substances, including moisturizers, sunscreens, and scents, can be included in lip products in addition to the many lip product kinds. When choosing a lip product, it’s crucial to take into account these substances to make sure it suits your demands and doesn’t have any negative side effects. In order to maintain your lips healthy and looking their best, you should exfoliate and moisturize them on a regular basis. The appropriate lip cosmetics can help you create attractive, healthy lips with a little knowledge and attention.

Different Types Of Backup Concentrate

Introduction to Backup Types

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Several types of Backup

As mentioned above, the types of backup concentrate more on “how” to back up rather than “what” to backup. Therefore, let’s study various types of backup in detail.

1. Full backup

It is the most classical or conventional way of backing up the entire spectrum of data right from files, subfolders, folders at the system level or data files, redo logs, procedures, control files at the database level.  The entire gamut of data will be backed up every time this full back up initiated.


A full backup is preferred in small setups where the consumption of backup storage is not that high.

It is Easy and simple to manage full backups.

Restoration is also easy and quick since it follows a straight and simple process.

The latest media is sufficient to restore full backups

It’s a slow and time-consuming process

Consumption of storage space is quite high and results in a lot of duplication of data.

System availability may be an issue to take frequent full backups.

2. Differential backup

This system involves backing up delta changes between the earlier full backup and current differential backup. Each time this backup is fired, the same process is repeated, which essentially means that files backed up in the earlier differential run in the cycle are also backed up, resulting in duplicates to some extent. Therefore, full backup media and the latest differential media are required to fully restore the system.


It takes lower storage media when compared to full backup

It is faster as it backs up only delta changes between earlier full backup and now

More frequent backups can be planned since it involves the lower volume of data to be backed up

Restoration is still faster when compared to incremental backup since it involves handling full backup media and the latest differential backup media.

When compared to incremental backup

It takes more storage space as it contains duplicates backed up in the earlier backups in the cycle.

Backing up is also slower since it handles a larger volume

When compared to full backup

Restoration is a little complicated as it had to use full backup and one more media.

The restoration takes more time for the above reason.

3. Incremental Backup

As in differential backup, a backup cycle starts with full backup and continues with multiple incremental backups. This system involves backing up incremental data created between the last backup and the current backup. For the first incremental backup, the last run is the full backup.

It isn’t easy this process through manual operation, and it is ideally managed through a vendor supplier tool or third party software.


It consumes the lowest storage media spaces because the volume of data to be backed up is low, and there are no duplicates in the data to be backed up.

Time taken for backup is also low due to the same above reason.

Many frequent backups can be planned, say daily, twice daily.

This system is used in database applications.

Restoration of data is very cumbersome as it involves full backup media and the subsequent incremental backups in the current cycle.

Restoration is also slower due to the above reason.

4. Mirror Backup Application Backup (Business Continuity Planning) Onsite model

In this set-up, a copy of the source application platform (primary) is maintained on a different floor or building in the same location as secondary. An initial full copy of the primary application is installed at the secondary site, and further changes in the primary are updated in the secondary site in synchronous mode by the application. Thus, it protects primary applications from any disaster due to hardware failures, corruption of databases, software failure, and other internal failures.

In case of any disaster, the application can be switched over to a secondary site without losing much time, and business continuity can be ensured. Moreover, the primary system can be rectified later and restored. However, it does not cover other disasters like power break down or natural furies like floods, earthquakes, and cyclones, affecting primary/secondary sites.

Offsite  model

In this model, the secondary site is maintained at an offsite location, and it is automatically updated at a frequent interval in an asynchronous mode. Therefore, it protects the primary site from natural disasters like floods, earthquakes, cyclones, political disturbances, etc.; the only issue in this model is primary and secondary sites should be interconnected with a high bandwidth network.

Cloud backup

It is a highly contemporary setup in which the backup of the data is done in the cloud, and the application can be switched over to the cloud if there is any disaster. Many cloud service providers offer this service, and it will result in cost savings if used prudently.


With so many options available for backing up their data, businesses will have to choose that method that suits their data strategy and budget and maintains business continuity.

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Types Of Memory In Java

Introduction to Types of Memory in Java

The Java virtual machine memory area is a runtime area that is used for the execution of various programs involved during runtime of a java application, the memory area of JVM is broadly divided into five different parts which are method area, heap area, Stack, Program counter (PC) registers area and Native method area. In this article, we will discuss the different types of memory in java.

Top 5 Types of Memory in Java

As we know that java is an object-oriented language; therefore, all objects created in java are stored in JVM (Java virtual machine). JVM memory is basically divided into the following parts:

1. Method Area 2. Heap Memory

Heap Memory in java is used by java runtime to allocate memory to objects and class during a java program’s execution. Whenever an object is created in java, it gets stored into heap memory. A garbage collection process runs on heap memory to free up unnecessary space that is garbage collection removes those objects from the heap area that does not have any references. Heap memory in java is divided into the following parts:

Young Generation: This is the part where all newly created objects are placed. When this part of the java heap gets filled up, a minor garbage collection occurs to free up space.

Old Generation: All objects left in memory after minor garbage collection are moved into the old generation. Therefore this is the part of heap memory where long-living objects are present.

Permanent Generation: This part of JVM contains native and static methods that provide metadata for running java applications.

Here are some important points regarding java heap memory:

If Heap space gets full, OutOfMemory error is thrown by java.

Access to Heap memory is slow as compared to stack memory.

Heap memory is much more in size as compared to stack memory.

Heap memory is not thread-safe as all objects share it.

Automatic deallocation is not present in heap memory as it needs a garbage collector to free up space.

3. Stack Memory

As the name signifies, stack memory is based on LIFO (last in, first out) principle. Stack memory is used for static memory allocation, and each executing thread in a java program has its own stack memory. Whenever a Java method is called, a new block is created in java stack memory to hold local or intermediate variables and references to other objects in the method. As soon as the execution of the method gets completed, the block of memory in the stack becomes empty and is used by the next method. The size of Stack memory is less as compared to heap memory. Here are some of the important features of stack memory.

Stack Memory grows and shrinks itself as new methods are added and removed to stack memory, respectively.

Stack memory gets automatically allocated and deallocated after the method completes its execution.

Access to stack memory is fast as compared to heap memory.

Whenever stack memory gets full, an exception called stack overflow exception is thrown by java.

Stack memory is thread-safe as each thread has its own stack memory.

4. PC Registers 5. Native Area Examples of Memory in Java

Now we will see a java example showing how memory is allocated:


package com.edubca.javademo; class StudentData { int rollNumber; String name; public StudentData(int rollNumber, String name) { super(); this.rollNumber = rollNumber; = name; } public int getRollNumber() { return rollNumber; } public void setRollNumber(int rollNumber) { this.rollNumber = rollNumber; } public String getName() { return name; } public void setName(String name) { = name; } } public class Main { public static void main(String[] args) { int id = 11; String name = "Yash"; StudentData s = null; s = new StudentData(id, name); System.out.println("Student Id is " + s.getRollNumber()); System.out.println("Student Name is " + s.getName()); } }


Memory Allocation:

Now we will see how memory is allocated in the above program:

In the Main class, after entering the main method, since id, the name is local variables a space in stack memory is created in the following way:

Integer id having primitive value will be stored in stack memory.

Reference of StudentData object s is stored in stack memory pointing to the original StudentData object, which is stored in heap memory.

Call to StudentData class constructor will further get added to the top of stack memory. The following will be stored:

Reference to calling object.

Integer variable id having value 11.

Reference of String type variable name which will point to an actual object stored in a string pool in heap memory.

Two instance variables with the name studentId and studentName declared in StudentData class will be stored in heap memory.

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Types Of Bias In Research

Research bias results from any deviation from the truth, causing distorted results and wrong conclusions. Bias can occur at any phase of your research, including during data collection, data analysis, interpretation, or publication. Research bias can occur in both qualitative and quantitative research.

Understanding research bias is important for several reasons.

Bias exists in all research, across research designs, and is difficult to eliminate.

Bias can occur at any stage of the research process.

Bias impacts the validity and reliability of your findings, leading to misinterpretation of data.

It is almost impossible to conduct a study without some degree of research bias. It’s crucial for you to be aware of the potential types of bias, so you can minimize them.

Example: Bias in researchSuppose that you are researching whether a particular weight loss program is successful for people with diabetes. If you focus purely on whether participants complete the program, you may bias your research.

For example, the success rate of the program will likely be affected if participants start to drop out (attrition). Participants who become disillusioned due to not losing weight may drop out, while those who succeed in losing weight are more likely to continue. This in turn may bias the findings towards more favorable results. 

Accounting for the differences between people who remain in a study and those who withdraw is important so as to avoid bias.

Information bias

Information bias, also called measurement bias, arises when key study variables are inaccurately measured or classified. Information bias occurs during the data collection step and is common in research studies that involve self-reporting and retrospective data collection. It can also result from poor interviewing techniques or differing levels of recall from participants.

The main types of information bias are:

Example: Information bias in researchYou are researching the correlation between smartphone use and musculoskeletal symptoms among high school students.

Over a period of four weeks, you ask students to keep a journal, noting how much time they spent on their smartphones along with any symptoms like muscle twitches, aches, or fatigue.

At the end of the study, you compare the self-reports with the usage data registered on their smartphones. You notice that for usage of less than three hours a day, self-reports tended to overestimate the duration of smartphone use. Conversely, for usage of more than three hours a day, self-reports tended to underestimate the duration of smartphone use. This goes to show that information bias can operate in more than one direction within a study group.

Recall bias

Recall bias is a type of information bias. It occurs when respondents are asked to recall events in the past and is common in studies that involve self-reporting.

As a rule of thumb, infrequent events (e.g., buying a house or a car) will be memorable for longer periods of time than routine events (e.g., daily use of public transportation). You can reduce recall bias by running a pilot survey and carefully testing recall periods. If possible, test both shorter and longer periods, checking for differences in recall.

Example: Recall bias in researchYou are conducting a case-control study examining the association between the diet of young children and the diagnosis of childhood cancer. You examine two groups:

A group of children who have been diagnosed, called the case group

A group of children who have not been diagnosed, called the control group

Since the parents are being asked to recall what their children generally ate over a period of several years, there is high potential for recall bias in the case group.

The best way to reduce recall bias is by ensuring your control group will have similar levels of recall bias to your case group. Parents of children who have childhood cancer, which is a serious health problem, are likely to be quite concerned about what may have contributed to the cancer.

Thus, if asked by researchers, these parents are likely to think very hard about what their child ate or did not eat in their first years of life. Parents of children with other serious health problems (aside from cancer) are also likely to be quite concerned about any diet-related question that researchers ask about.

Therefore, these parents can be expected to recall their children’s diet in a way that is more comparable with parents of children who have cancer. In contrast, parents of children who have no health problems or parents of children with only minor health problems are less likely to be concerned with carefully recalling their children’s eating habits.

Observer bias

Observer bias is the tendency of research participants to see what they expect or want to see, rather than what is actually occurring. Observer bias can affect the results in observational and experimental studies, where subjective judgment (such as assessing a medical image) or measurement (such as rounding blood pressure readings up or down) is part of the data collection process.

Example: Observer bias in researchYou and a colleague are investigating communication behavior in a hospital. You are observing eight doctors and two nurses, seeking to discover whether they prefer interruptive communication mechanisms—face-to-face discussion or telephone calls—over less interruptive methods, such as emails. You and your colleague follow the 10 members of staff for a month. Each time you observe someone making a call, walking to a colleague to ask something, etc., you make a note.

Based on discussions you had with other researchers before starting your observations, you are inclined to think that medical staff tend to simply call each other when they need specific patient details or have questions about treatments.

At the end of the observation period, you compare notes with your colleague. Your conclusion was that medical staff tend to favor phone calls when seeking information, while your colleague noted down that medical staff mostly rely on face-to-face discussions. Seeing that your expectations may have influenced your observations, you and your colleague decide to conduct semi-structured interviews with medical staff to clarify the observed events.

Note:Observer bias and actor–observer bias are not the same thing.

Observer bias arises from the opinions and expectations of the observer, influencing data collection and recording, while actor–observer bias has to do with how we interpret the same behavior differently depending on whether we are engaging in it or others are.

Performance bias

Performance bias is unequal care between study groups. Performance bias occurs mainly in medical research experiments, if participants have knowledge of the planned intervention, therapy, or drug trial before it begins.

Studies about nutrition, exercise outcomes, or surgical interventions are very susceptible to this type of bias. It can be minimized by using blinding, which prevents participants and/or researchers from knowing who is in the control or treatment groups. If blinding is not possible, then using objective outcomes (such as hospital admission data) is the best approach.

Example: Performance bias in researchYou are investigating whether a high-protein diet can help people lose weight. The experimental group is provided with a high-protein meal plan, while the control group follows their regular diet. The control group does not know that the study is about the link between protein and weight loss, but they can easily guess that it’s about nutrition. If participants know this beforehand, they can potentially change their behavior, e.g., by increasing their protein intake or seeking to eat more healthily than they normally do.

Regression to the mean (RTM)

Regression to the mean (RTM) is a statistical phenomenon that refers to the fact that a variable that shows an extreme value on its first measurement will tend to be closer to the center of its distribution on a second measurement.

Medical research is particularly sensitive to RTM. Here, interventions aimed at a group or a characteristic that is very different from the average (e.g., people with high blood pressure) will appear to be successful because of the regression to the mean. This can lead researchers to misinterpret results, describing a specific intervention as causal when the change in the extreme groups would have happened anyway.

Example: Regression to the mean (RTM)You are researching a new intervention for people with depression.

In general, among people with depression, certain physical and mental characteristics have been observed to deviate from the population mean.

This could lead you to think that the intervention was effective when those treated showed improvement on measured post-treatment indicators, such as reduced severity of depressive episodes.

However, given that such characteristics deviate more from the population mean in people with depression than in people without depression, this improvement could be attributed to RTM.

In order to differentiate between RTM and true improvement, consider introducing a control group, such as an untreated group of similar individuals or a group of similar individuals in an alternative treatment.

Interviewer bias

Interviewer bias stems from the person conducting the research study. It can result from the way they ask questions or react to responses, but also from any aspect of their identity, such as their sex, ethnicity, social class, or perceived attractiveness.

Interviewer bias distorts responses, especially when the characteristics relate in some way to the research topic. Interviewer bias can also affect the interviewer’s ability to establish rapport with the interviewees, causing them to feel less comfortable giving their honest opinions about sensitive or personal topics.

Example: Interviewer bias in researchSuppose you are interviewing people about how they spend their free time at home.

Participant: “I like to solve puzzles, or sometimes do some gardening.”

You: “I love gardening, too!”

In this case, seeing your enthusiastic reaction could lead the participant to talk more about gardening.

Establishing trust between you and your interviewees is crucial in order to ensure that they feel comfortable opening up and revealing their true thoughts and feelings. At the same time, being overly empathetic can influence the responses of your interviewees, as seen above.

A better approach here would be to use neutral responses that still show that you’re paying attention and are engaged in the conversation. Some examples could include “Thank you for sharing” or “Can you tell me more about that?”

Publication bias

Publication bias occurs when the decision to publish research findings is based on their nature or the direction of their results. Studies reporting results that are perceived as positive, statistically significant, or favoring the study hypotheses are more likely to be published due to publication bias.

Example: Publication bias in researchA researcher testing a drug for Alzheimer’s discovers that there is no statistically significant difference between the patients in the control and the treatment groups. Fearing that this will impact their chances for securing funding and career promotion, they decide not to publish their findings.

Researcher bias

Researcher bias occurs when the researcher’s beliefs or expectations influence the research design or data collection process. Researcher bias can be deliberate (such as claiming that an intervention worked even if it didn’t) or unconscious (such as letting personal feelings, stereotypes, or assumptions influence research questions).

The unconscious form of researcher bias is associated with the Pygmalion effect (or Rosenthal effect), where the researcher’s high expectations (e.g., that patients assigned to a treatment group will succeed) lead to better performance and better outcomes.

NoteAlthough researcher bias and observer bias may seem similar, they are not the same thing. Observer bias affects how behaviors are recorded or measurements are taken, often in the data collection and interpretation stages. Researcher bias is a broader term and can influence any part of the research design.

Researcher bias is also sometimes called experimenter bias, but it applies to all types of investigative projects, rather than only to experimental designs.

Example: Researcher bias Suppose you want to study the effects of alcohol on young adults. If you are already convinced that alcohol causes young people to behave in a reckless way, this may influence how you phrase your survey questions. Instead of being neutral and non-judgmental, they run the risk of reflecting your preconceived notions around alcohol consumption. As a result, your survey will be biased.

Good question: What are your views on alcohol consumption among your peers?

Bad question: Do you think it’s okay for young people to drink so much?

Response bias

Response bias is a general term used to describe a number of different situations where respondents tend to provide inaccurate or false answers to self-report questions, such as those asked on surveys or in structured interviews.

This happens because when people are asked a question (e.g., during an interview), they integrate multiple sources of information to generate their responses. Because of that, any aspect of a research study may potentially bias a respondent. Examples include the phrasing of questions in surveys, how participants perceive the researcher, or the desire of the participant to please the researcher and to provide socially desirable responses.

Response bias also occurs in experimental medical research. When outcomes are based on patients’ reports, a placebo effect can occur. Here, patients report an improvement despite having received a placebo, not an active medical treatment.

Example: Response biasYou are researching factors associated with cheating among college students.

While interviewing a student, you ask them:

“Do you think it’s okay to cheat on an exam?”

Since cheating is generally regarded as a bad thing, the word itself is negatively charged. Here, the student may feel the need to hide their true feelings, conforming to what is considered most socially acceptable—that cheating is not okay.

Common types of response bias are:

Acquiescence bias

Acquiescence bias is the tendency of respondents to agree with a statement when faced with binary response options like “agree/disagree,” “yes/no,” or “true/false.” Acquiescence is sometimes referred to as “yea-saying.”

This type of bias occurs either due to the participant’s personality (i.e., some people are more likely to agree with statements than disagree, regardless of their content) or because participants perceive the researcher as an expert and are more inclined to agree with the statements presented to them.

Example: Acquiescence bias in researchSuppose you are researching introversion and extroversion among students. You include the following question in your survey:

Q: Are you a social person?



People who are inclined to agree with statements presented to them are at risk of selecting the first option, even if it isn’t fully supported by their lived experiences.

In order to control for acquiescence, consider tweaking your phrasing to encourage respondents to make a choice truly based on their preferences. Here’s an example:

Q: What would you prefer?

A quiet night in

A night out with friends

Demand characteristics

Demand characteristics are cues that could reveal the research agenda to participants, risking a change in their behaviors or views. Ensuring that participants are not aware of the research objectives is the best way to avoid this type of bias.

Example: Demand characteristicsA researcher is investigating whether a spinal operation can reduce back pain complaints. Patients are interviewed by the surgeon who conducted the operation six weeks, three months, and one year post-op, and their levels of pain are assessed.

Sensing this, the patients downplayed any complaints in an effort to please the researcher. The researcher’s frowns served as cues (demand characteristics) that helped participants figure out that the research agenda was lessened pain.

Social desirability bias

Social desirability bias is the tendency of participants to give responses that they believe will be viewed favorably by the researcher or other participants. It often affects studies that focus on sensitive topics, such as alcohol consumption or sexual behavior.

Example: Social desirability biasYou are designing an employee well-being program for a technology start-up. You want to gauge employees’ interest in various activities and components that could be included in this program.

You are conducting face-to-face semi-structured interviews with a number of employees from different departments. When asked whether they would be interested in a smoking cessation program, there was widespread enthusiasm for the idea.

However, when you leave the building at the end of the day, you run into a few members of the interview group smoking outside. You overhear them saying how they don’t like the idea of the smoking cessation program, but they felt they couldn’t really say it because smoking is considered a bad habit in this day and age.

Note that while social desirability and demand characteristics may sound similar, there is a key difference between them. Social desirability is about conforming to social norms, while demand characteristics revolve around the purpose of the research.

Courtesy bias

Courtesy bias stems from a reluctance to give negative feedback, so as to be polite to the person asking the question. Small-group interviewing where participants relate in some way to each other (e.g., a student, a teacher, and a dean) is especially prone to this type of bias.

Example: Courtesy biasYou are researching cases of disrespectful behavior towards women who gave birth at hospitals. If you ask women about their experiences while in or near the facility in which they received care, it is possible that some women may avoid giving negative feedback.

Courtesy bias, including fear of repercussions, may lead some women to avoid sharing any negative experiences. Conducting interviews to capture women’s experiences of disrespect in a more neutral setting is the best approach here.

Question order bias

Question order bias occurs when the order in which interview questions are asked influences the way the respondent interprets and evaluates them. This occurs especially when previous questions provide context for subsequent questions.

When answering subsequent questions, respondents may orient their answers to previous questions (called a halo effect), which can lead to systematic distortion of the responses.

Example: Question order biasIf a respondent is asked how satisfied he is with his marriage, this increases the probability that he will also take his marriage into account when answering the question about his satisfaction with life in general.

In this case, you can minimize question order bias by asking general questions (satisfaction with life) prior to specific ones (marriage).

Extreme responding

Extreme responding is the tendency of a respondent to answer in the extreme, choosing the lowest or highest response available, even if that is not their true opinion. Extreme responding is common in surveys using Likert scales, and it distorts people’s true attitudes and opinions.

Disposition towards the survey can be a source of extreme responding, as well as cultural components. For example, people coming from collectivist cultures tend to exhibit extreme responses in terms of agreement, while respondents indifferent to the questions asked may exhibit extreme responses in terms of disagreement.

Example: Extreme respondingYou want to find out what students think of on-campus counseling services via a survey using Likert scales. There are 40 questions, with potential responses ranging from “strongly agree” to “strongly disagree.”

In your pilot study, you notice that a number of respondents only select the extreme options for each question. To mitigate this, you decide to shorten the questionnaire and diversify the questions. Instead of solely using Likert scales, you also add some multiple-choice and open questions.

Selection bias

Selection bias is a general term describing situations where bias is introduced into the research from factors affecting the study population.

Common types of selection bias are:

Example: Selection biasYou are investigating elderly people’s self-perceived physical health in your city. You go outside a public pool and interview elderly people as they exit.

Collecting your data only from senior citizens at the pool will lead to selection bias in your data. In this case, you are excluding elderly people who are not willing or able to maintain an active lifestyle.

Sampling or ascertainment bias

Sampling bias occurs when your sample (the individuals, groups, or data you obtain for your research) is selected in a way that is not representative of the population you are analyzing. Sampling bias threatens the external validity of your findings and influences the generalizability of your results.

The easiest way to prevent sampling bias is to use a probability sampling method. This way, each member of the population you are studying has an equal chance of being included in your sample.

Sampling bias is often referred to as ascertainment bias in the medical field.

Example: Sampling (or ascertainment) biasYou are researching the likelihood of heart disease in your area. You decide to collect your data by interviewing people entering and leaving a local shopping mall.

This collection method does not include people who are bedridden or very ill from heart disease. Because many of them are more likely confined at their homes or in a hospital, and not walking around a mall, your sample is biased.

Attrition bias

Attrition bias occurs when participants who drop out of a study systematically differ from those who remain in the study. Attrition bias is especially problematic in randomized controlled trials for medical research because participants who do not like the experience or have unwanted side effects can drop out and affect your results.

You can minimize attrition bias by offering incentives for participants to complete the study (e.g., a gift card if they successfully attend every session). It’s also a good practice to recruit more participants than you need, or minimize the number of follow-up sessions or questions.

Example: Attrition bias in researchUsing a longitudinal design, you investigate whether a stress management training program can help students with anxiety regulate their stress levels during exams.

You provide a treatment group with weekly one-hour sessions over a two-month period, while a control group attends sessions on an unrelated topic. You complete five waves of data collection to compare outcomes: a pretest survey, three surveys during the program, and a posttest survey.

During your study, you notice that a number of participants drop out, failing to attend the training sessions or complete the follow-up surveys. You check the baseline survey data to compare those who leave against those who remain, finding that participants who left reported significantly higher levels of anxiety than those who stayed. This means your study has attrition bias.

Self-selection or volunteer bias

Self-selection bias (also called volunteer bias) occurs when individuals who volunteer for a study have particular characteristics that matter for the purposes of the study.

Closely related to volunteer bias is nonresponse bias, which occurs when a research subject declines to participate in a particular study or drops out before the study’s completion.

Example: Self-selection (or volunteer) bias in researchYou want to study whether fish consumption can reduce the risk of cognitive decline in elderly people. In order to recruit volunteers, you place posters in the area around the hospital where the experiment will take place.

Considering that the hospital is located in an affluent part of the city, volunteers are more likely to have a higher socioeconomic standing, higher education, and better nutrition than the general population.

This means that volunteer bias may affect your findings as the participants will differ significantly from non-participants in ways that relate to the study objectives (i.e., the relationship between nutrition and cognitive decline).

Survivorship bias

Survivorship bias occurs when you do not evaluate your data set in its entirety: for example, by only analyzing the patients who survived a clinical trial.

This strongly increases the likelihood that you draw (incorrect) conclusions based upon those who have passed some sort of selection process—focusing on “survivors” and forgetting those who went through a similar process and did not survive.

Note that “survival” does not always mean that participants died! Rather, it signifies that participants did not successfully complete the intervention.

Example: Survivorship bias in researchYou are researching which factors contribute to a successful career as an entrepreneur. Looking into the résumés of well-known entrepreneurs, you notice that most of them were college dropouts. This could make you think that having a good idea and leaving college to pursue it is all that it takes to set off your career.

However, most college dropouts do not become billionaires. In fact, there are many more aspiring entrepreneurs who dropped out of college to start companies and failed than succeeded.

When you focus on the people who left school and succeeded, ignoring the far larger group of dropouts who did not, you are succumbing to survivorship bias. This means that a visible “successful” subgroup is mistaken as an entire group due to the “failure” subgroup’s not being visible.

Nonresponse bias

Nonresponse bias occurs when those who do not respond to a survey or research project are different from those who do in ways that are critical to the goals of the research. This is very common in survey research, when participants are unable or unwilling to participate due to factors like lack of the necessary skills, lack of time, or guilt or shame related to the topic.

You can mitigate nonresponse bias by offering the survey in different formats (e.g., an online survey, but also a paper version sent via post), ensuring confidentiality, and sending them reminders to complete the survey.

Example: Nonresponse bias in researchYou are investigating the average age of people in your city who have a landline in their homes. You attempt to conduct a phone survey of 1,000 individuals, dialed randomly from the population of landline-owning residents. After 1,000 attempts, you are in possession of 746 valid responses, while 254 individuals never answered the phone. Is this sample representative?

You notice that your surveys were conducted during business hours, when the working-age residents were less likely to be home.

If working-age respondents are underrepresented in your sample, then the average among the 746 valid age responses will skew older than the true population average. In this case, the difference between the biased average and the true, but unobserved, average age among all landline owners is due to nonresponse bias.

Undercoverage bias

Undercoverage bias occurs when you only sample from a subset of the population you are interested in. Online surveys can be particularly susceptible to undercoverage bias. Despite being more cost-effective than other methods, they can introduce undercoverage bias as a result of excluding people who do not use the internet.

Example: Undercoverage bias in researchYou are running a web survey on self-reported health, focusing on smoking habits and binge drinking. However, the method of your survey means that you are systematically excluding non-internet users. Undercoverage would not be a problem here if internet users did not differ from non-internet users.

However, you know from previous studies that the proportion of non-internet use has a positive relationship with age and a negative relationship with education level. This means that you run a risk of excluding older and less educated respondents from your sample. Since the differences between internet users and non-internet users can play a significant role in influencing your study variables, you will not be able to draw valid conclusions from your web survey.

Cognitive bias

Cognitive bias refers to a set of predictable (i.e., nonrandom) errors in thinking that arise from our limited ability to process information objectively. Rather, our judgment is influenced by our values, memories, and other personal traits. These create “ mental shortcuts” that help us process information intuitively and decide faster. However, cognitive bias can also cause us to misunderstand or misinterpret situations, information, or other people.

Example: Cognitive biasWhen asked if their favorite team will win a game, most people will be positive. However, fewer people will still claim that they had predicted a win when asked again after their team has lost. Most people will likely say that they knew it all along the team was going to lose.

Because of cognitive bias, people often perceive events to be more predictable after they happen.

When something happens, it often seems so obvious that we should have seen it coming, and that may distort our memories so that our earlier predictions conform with this belief. In other words, we tell ourselves “I knew it all along”.

Although there is no general agreement on how many types of cognitive bias exist, some common types are:

Anchoring bias

Anchoring bias is people’s tendency to fixate on the first piece of information they receive, especially when it concerns numbers. This piece of information becomes a reference point or anchor. Because of that, people base all subsequent decisions on this anchor. For example, initial offers have a stronger influence on the outcome of negotiations than subsequent ones.

Example: Anchoring bias Anchoring bias can greatly influence the estimated value of a product.

If a car salesperson starts negotiations at $12,000, you’ll likely think you’re getting a good deal when you eventually agree with him to lower the price to $10,500. The real value of the car may be near $10,000, but the first figure you heard influenced your estimation of its value.

Framing effect

Framing effect refers to our tendency to decide based on how the information about the decision is presented to us. In other words, our response depends on whether the option is presented in a negative or positive light, e.g., gain or loss, reward or punishment, etc. This means that the same information can be more or less attractive depending on the wording or what features are highlighted.

Example: Framing effectThe framing effect strongly influences our acceptance of information. A medical procedure with a 90% chance of survival sounds more appealing than one with a 10% chance of mortality. This is because most people prefer an outcome that is presented in a positive rather than a negative way, even though it is the same information or issue, simply cast in a different light.

Actor–observer bias

Actor–observer bias occurs when you attribute the behavior of others to internal factors, like skill or personality, but attribute your own behavior to external or situational factors.

In other words, when you are the actor in a situation, you are more likely to link events to external factors, such as your surroundings or environment. However, when you are observing the behavior of others, you are more likely to associate behavior with their personality, nature, or temperament.

Example: Actor–observer bias in researchSuppose you are researching road rage. You are interviewing people about their driving behavior, as well as the behavior of others.

One interviewee recalls a morning when it was raining heavily. They were rushing to drop off their kids at school in order to get to work on time. As they were driving down the highway, another car cut them off as they were trying to merge. They tell you how frustrated they felt and exclaim that the other driver must have been a very rude person.

At another point, the same interviewee recalls that they did something similar: accidentally cutting off another driver while trying to take the correct exit. However, this time, the interviewee claimed that they always drive very carefully, blaming their mistake on poor visibility due to the rain.

This interview was influenced by actor–observer bias. Your interviewee attributed internal factors (rudeness) to others and external factors (rain) to themselves while describing identical behavior (driving dangerously).

Availability heuristic

Availability heuristic (or availability bias) describes the tendency to evaluate a topic using the information we can quickly recall to our mind, i.e., that is available to us. However, this is not necessarily the best information, rather it’s the most vivid or recent. Even so, due to this mental shortcut, we tend to think that what we can recall must be right and ignore any other information.

Example: Availability heuristic After reading many news stories about shark attacks, you start thinking these attacks always happen. When you go on holiday, you hesitate to swim in the ocean, because you are convinced it’s risky.

Confirmation bias

Confirmation bias is the tendency to seek out information in a way that supports our existing beliefs while also rejecting any information that contradicts those beliefs. Confirmation bias is often unintentional but still results in skewed results and poor decision-making.

Example: Confirmation bias in researchYou are a social scientist researching how military families handle long-term, overseas family separation.

Let’s say you grew up with a parent in the military. Chances are that you have a lot of complex emotions around overseas deployments. This can lead you to over-emphasize findings that “prove” that your lived experience is the case for most families, neglecting other explanations and experiences.

As a researcher, it’s critical to make evidence-based decisions when supporting or rejecting a hypothesis and to avoid acting with confirmation bias towards a given outcome.

Halo effect

The halo effect refers to situations whereby our general impression about a person, a brand, or a product is shaped by a single trait. It happens, for instance, when we automatically make positive assumptions about people based on something positive we notice, while in reality, we know little about them.

Example: Halo effectWhen we meet someone for the first time, the information we have about them is limited, so we will use any information available to assess their character. In that process, we focus e.g., on observable behaviors like body language and facial expressions. We may notice that they seem friendly because they smile a lot and from that assume other positive characteristics about that person, such as that they must be kind or intelligent.

The Baader-Meinhof phenomenon

The Baader-Meinhof phenomenon (or frequency illusion) occurs when something that you recently learned seems to appear “everywhere” soon after it was first brought to your attention. However, this is not the case. What has increased is your awareness of something, such as a new word or an old song you never knew existed, not their frequency.

Example: The Baader-Meinhof phenomenon Soon after hearing the word “simulacrum” in a philosophy class, you experience immediately encountering it again: as the title of a theatrical play, a video game, and a metal band by the same name, over a short period. You start to think this is a strange coincidence. When your brain learns something new and interesting, you simply tend to notice it more.

How to avoid bias in research

While very difficult to eliminate entirely, research bias can be mitigated through proper study design and implementation. Here are some tips to keep in mind as you get started.

Clearly explain in your methodology section how your research design will help you meet the research objectives and why this is the most appropriate research design.

In quantitative studies, make sure that you use probability sampling to select the participants. If you’re running an experiment, make sure you use random assignment to assign your control and treatment groups.

Account for participants who withdraw or are lost to follow-up during the study. If they are withdrawing for a particular reason, it could bias your results. This applies especially to longer-term or longitudinal studies.

Use triangulation to enhance the validity and credibility of your findings.

Phrase your survey or interview questions in a neutral, non-judgmental tone. Be very careful that your questions do not steer your participants in any particular direction.

Consider using a reflexive journal. Here, you can log the details of each interview, paying special attention to any influence you may have had on participants. You can include these in your final analysis.

Other types of research bias Frequently asked questions about research bias

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