Ascertainment bias is a form of selection bias and is related to sampling bias. In medical research, the term ascertainment bias is more common than the term sampling bias.

What is ascertainment bias?

Ascertainment bias is a form of systematic error that occurs during data collection and analysis. It occurs when sample units are drawn in such a way that those selected are not representative of the target population.

In medical research, ascertainment bias also refers to situations where the results of a clinical trial are distorted due to knowledge about which intervention each participant is receiving.

Ascertainment bias can be introduced by:

• The person administering the intervention
• The person receiving the intervention
• The investigator assessing or analyzing the outcomes
• The report writer describing the trial in detail

Ascertainment bias can influence the generalizability of your results and threaten the external validity of your findings.

There are two main sources of ascertainment bias:

1. Data collection: Ascertainment bias is an inherent problem in non-probability sampling designs like convenience samples and self-selection samples. These samples are often biased, and inferences based on them are not as trustworthy as when a random sample is used.

2. Lack of blinding: In experimental designs, it is important that neither the researchers nor the participants know participant group assignments. For example, if a participant knows that they are receiving a placebo, they are less likely to report benefits related to the placebo effect. As a result, the comparison between the treatment and the control group will be distorted.

Ascertainment bias examples

Blinding is an important methodological feature of placebo-controlled trials to minimize research bias and maximize the validity of the research results.

How to prevent ascertainment bias

In experimental studies, ascertainment bias can be reduced by “blinding” everyone involved, including those who administer the intervention, those who receive it, and those concerned with assessing and reporting the results. This is called triple blinding.

More specifically, ascertainment bias can be avoided in the following ways during the data collection phase:

• When a placebo is compared to an active treatment, the two drugs should be similar in taste, smell, and appearance. They should also be delivered using the same procedure and in the same packaging. In this way, study participants and researchers won’t realize which drug the patient is taking.

• The person arranging the randomization (i.e., which patient takes which drug) should have no other involvement in the study. They should not reveal to anyone else involved in the study which patient is taking which drug. This also goes for researchers involved in assessing the outcomes.

This also reduces the risk of introducing other types of bias, such as demand characteristics and confirmation bias.

Keep in mind that bias can also be introduced after data collection. To reduce ascertainment bias in this phase, make sure that:

• Participants remain anonymous
• The coding of the study groups is done prior to providing the data to the researchers responsible for the analysis and reporting of the results
• The codes remain undisclosed until the process of analysis and reporting of the trial is completed

Lastly, ascertainment bias can also affect observational studies because subjects cannot be randomized. In this case, you can reduce ascertainment bias by carefully describing the inclusion and exclusion criteria used for selecting subjects or cases.

Frequently asked questions

Why is bias in research a problem?

Bias affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where new forms of treatment are being evaluated.

What are common types of selection bias?

Common types of selection bias are:

• Sampling or ascertainment bias
• Attrition bias
• Volunteer or self-selection bias
• Survivorship bias
• Non-response bias
• Undercoverage bias

What’s the difference between reliability and validity?

Reliability and validity are both about how well a method measures something:

• Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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The placebo effect is often observed in experimental designs where participants are randomly assigned to either a control or treatment group. 

What is a placebo?

A placebo can be a sugar pill, a salt water injection, or even a fake surgical procedure. In other words, a placebo has no therapeutic properties. Placebos are often used in medical research and clinical trials to help scientists evaluate the effects of new medications.

In these clinical trials, participants are randomly assigned to either the placebo or the experimental medication. Crucially, they are not aware of which treatment they receive. The results of the two groups are compared, to see whether they differ.

In double-blind studies, researchers also don’t know who received the actual treatment or the placebo. This is to prevent them from conveying demand characteristics to participants that could influence the study’s results. This is preferred over single-blind studies, where participants do not know which group they have been placed in, but researchers do.

Placebos may help relieve symptoms like pain, fatigue, or stress-related insomnia, but they don’t actually treat a condition or cure a disease. Note that due to ethical considerations, placebos are not always used in clinical trials. For example, as it would be unethical to leave terminal cancer patients untreated, placebos aren’t used in these types of studies.

What is the placebo effect?

For some people, just the idea that they are taking medication makes them feel better. This occurs even if the medication is actually just a placebo. This phenomenon is known as the placebo effect. In other words, the perception of feeling better is triggered by the person’s belief in the benefit of the treatment.

When studying a new treatment, researchers must demonstrate that it is more effective than can just be explained by the placebo effect. To do so, they compare the results from those taking the new treatment are with those from the placebo. In order to accurately compare the two groups, participants in clinical trials must not know whether they received the treatment or the placebo. If the two groups have the same reaction, the new treatment is deemed ineffective.

When evaluating the effectiveness of the new treatment, researchers then exclude the placebo effect. Studies on the placebo effect indicate that 21% to 40% of study participants taking placebos will experience relief from symptoms.

How does the placebo effect work?

Although the exact reasons for the positive effects of placebos are still being researched, a number of factors contribute to the phenomenon. These include:

• A person’s expectations or beliefs that they will get better. People who are motivated and expect their treatment to work are more likely to experience the placebo effect.
• The feeling of receiving attention and care due to participation in the study. This may reduce stress levels and trigger the body’s own pain-relieving chemicals.
• Classical conditioning, or the association people build over the years between a certain action, such as pill-taking, and positive results.
• A trusting relationship between doctors and patients or researchers and study participants from the sample. Listening to an expert you trust talk enthusiastically about a treatment can impact how you respond to it.

However, researchers do not attribute the placebo effect exclusively to psychology. A few other possible explanations include:

• Regression to the mean: When people first visit a doctor or start on a clinical trial, their symptoms might be particularly bad. But in the natural course of an illness, symptoms may subside on their own.
• Confirmation bias: Feelings of hopefulness about a new treatment may lead people to pay more attention to signs that they’re getting better and less attention to signs that they’re getting worse.

Placebo effect examples

The placebo effect illustrates how the mind can trigger changes in the body.

The placebo effect can also explain the popularity of non-FDA-approved products.

Downside of the placebo effect

The response of people assigned to the placebo control group may not always be positive. They may experience what is called a “nocebo effect,” or a negative outcome, when taking a placebo. The same explanation applies here. If you expect a negative outcome, it’s more likely you’ll have a negative outcome.

For example, in a clinical trial, participants who are given a placebo but are told what side effects the “treatment” may cause. They may have the same side effects as the participants who are given the active treatment, only because they expect them to occur.

Frequently asked questions about the placebo effect

What causes the placebo effect?

Although there is no definite answer to what causes the placebo effect, researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Why are placebos used in research?

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.

Why is bias in research a problem?

Bias affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where new forms of treatment are being evaluated.

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Regression to the mean is due to natural variation or chance. It can be observed in everyday life, particularly in research that intentionally focuses on the most extreme cases or events. It is sometimes also called regression toward the mean.

Regression to the mean is common in repeated measurements (within-subject designs) and should always be considered as a possible cause of an observed change. It is considered a type of information bias and can distort research findings.

What is regression to the mean?

Regression to the mean is observed when variables that are extremely higher or extremely lower than average on the first measurement move closer to the average on the second measurement.

In general, RTM explains why unusual events are likely to be followed by more typical ones. Suppose that a company has a great quarter, exceeding all targets set. As exceptional performance is difficult to maintain over time, there is a high chance of worse performance in the next quarter, shifting the performance of the company back towards the mean. Anything that can be influenced by an element of chance is subject to this phenomenon.

Regression to the mean occurs when a nonrandom sample is selected from a population and you measure two imperfectly correlated variables, such as two consecutive blood pressure measurements.

• The smaller the correlation between the two variables, the larger the effect of RTM.
• The more extreme the value from the population mean, the more room there is to regress to the mean.

Regression to the mean can be explained by considering, for example, that skill and performance are imperfectly correlated due to the role of luck. This may lead you to find a causal relationship where there isn’t one.

Why is regression to the mean a problem?

Regression to the mean can prove problematic particularly in research studies that measure the effectiveness of an intervention, program, or policy.

It can mislead researchers to believe that an intervention is the cause of an observed change, when in reality it is due to chance. This is particularly evident when researchers focus on measurements of people, cases, or organizations at the extremes, such as the worst-performing, the best-educated, or the unhealthiest.

RTM shows us that, statistically, the lowest cases are likely to improve the second time, while those at their peak will likely perform worse even without the intervention. Because it can distort results, you need to take regression to the mean into account when designing your research as well as when analyzing your findings.

Otherwise, you run the risk of attributing certain results to a particular cause, when in reality they were most likely due to chance.

Regression to the mean examples

Regression to the mean often happens when measuring the effects of an intervention.

Relatedly, randomized evaluations are essential in avoiding regression to the mean when estimating the effects of an intervention.

How to avoid regression to the mean

The best way to avoid regression to the mean is to account for it during the design phase of your research. Whenever possible, use a probability sampling method. Otherwise, your results may lean towards the extremes, either abnormally high or abnormally low for the average. These will, by design, regress towards the mean upon retesting.

In experimental designs, it is important to use a control group and a treatment group, randomly assigning participants to each one. Changes in the control group can help you evaluate the extent of changes caused by regression to the mean, in addition to any placebo effect. Any extra improvement or deterioration in the experimental group compared to the control group can be attributed to the intervention, so long as it is statistically significant.

Calculating the percent of regression to the mean

Alternatively, you can calculate the percent of regression to the mean during your data analysis. You can use the formula below to calculate regression to the mean.

Frequently asked questions

What are the main types of information bias?

Information bias is a general term describing various forms of research bias arising due to systematic measurement error. The main types of information bias are:

• Recall bias
• Observer bias
• Performance bias
• Regression to the mean (RTM)

What is a correlation coefficient?

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. The Pearson product-moment correlation coefficient (Pearson’s r) is commonly used to assess a linear relationship between two quantitative variables.

Why doesn’t correlation imply causation?

The third variable and directionality problems are two main reasons why correlation isn’t causation.

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

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Like other pronouns, they are used in place of nouns to allow us to speak and write more concisely. For example, without the first-person pronoun “I,” you would have to use your name every time you wanted to make a statement about yourself.

Personal pronouns change form based on person, number, gender, and case, as shown in the table below.

Person, number, gender, and case

Four factors indicate which personal pronoun you should use in a particular grammatical context to refer to a particular person or thing. These are:

• Person (first, second, or third)
• Number (singular or plural)
• Gender (masculine, feminine, neuter, or epicene)
• Case (subject, object, possessive, or reflexive)

Person

Personal pronouns are called “personal” not because they always refer to people (“it,” for example, doesn’t) but because they indicate a grammatical feature called person. There are three possibilities:

• First-person pronouns are used to refer to the person speaking or writing.
• Second-person pronouns are used to refer to the person being addressed.
• Third-person pronouns are used to refer to anyone or anything else.

Number

Number indicates whether the personal pronoun refers to an individual person or thing or to a group of two or more.

• Singular pronouns refer to an individual person or thing.
• Plural pronouns refer to two or more people or things.

Gender

Gender is how personal pronouns indicate the gender of the person referred to—or the lack of gender of objects and concepts.

• Masculine pronouns refer to men and boys.
• Feminine pronouns refer to women and girls.
• Gender-neutral pronouns, also referred to as epicene, refer to people who identify as neither male nor female, or to people whose gender is not considered relevant in context.
• Neuter pronouns refer to things that are not people: objects, concepts, places, and sometimes animals.

Case

Case means the grammatical role that the pronoun plays in a sentence. Personal pronouns can play four different roles in a sentence:

• Subject pronouns act as the subject of a verb—the person or thing that performs the action described.
• Object pronouns act as the object of a verb or preposition—the person or thing that is acted upon.
• Possessive pronouns indicate ownership—they stand for something or someone that belongs to someone or something else. These are slightly different from possessive determiners.
• Reflexive pronouns are used instead of object pronouns when when the object of the sentence is the same as the subject.

First-person pronouns

The first-person pronouns don’t vary based on gender, but they do vary based on number and case, as shown in the table below. The first-person singular subject pronoun “I” is the only English pronoun that is always capitalized.

Note that there is ongoing debate about the use of first-person pronouns in academic writing.

Second-person pronouns

The second-person pronouns also do not vary based on gender, only the reflexive form varies based on number, and the subject and object forms are the same. This makes them the least variable set of personal pronouns but can occasionally lead to ambiguity.

Second-person pronouns should almost never be used in academic writing, as addressing the reader directly is seen as too informal.

Third-person pronouns

The third-person pronouns are much more variable than the first- and second-person pronouns, since they also change form based on gender, in addition to number and case.

As well as forms for the masculine and feminine, there is a neuter (or inanimate) form that’s used to refer to things other than people (e.g., ideas, objects, animals).

There’s also an increasingly widely used gender-neutral (or epicene) form, the singular “they.” This is largely identical to the plural form (which is always gender-neutral), except that the reflexive “themself” is sometimes used instead of “themselves” (though it’s often considered nonstandard).

Impersonal pronouns

The impersonal pronoun “one,” as the name suggests, doesn’t vary based on person—it’s not in the first, second, or third person.

Rather, like an indefinite pronoun, it refers to a nonspecific, generic individual, usually for the purpose of making a generalization or stating a principle. It’s considered quite formal and often replaced with “you,” or otherwise avoided, in informal contexts.

The impersonal pronoun doesn’t vary based on number or gender, and it has the same form whether used as a subject or object. It does have a separate form for the reflexive, but no possessive pronoun form.

Other personal pronouns

There are a few other personal pronouns that are rarely used, nonstandard, or archaic (no longer used). These generally shouldn’t show up in your academic or formal writing, but it’s worth knowing they exist.

Archaic second-person pronouns

The lack of variety in English second-person pronouns is somewhat unusual, as other languages (e.g., French) make clearer distinctions between singular and plural and between formal and informal ways of addressing someone.

Early Modern English used a larger set of second-person pronouns to convey this kind of distinction. The “th” pronouns were used for informal address, while the “y” pronouns were used for both formal address and plurals.

These additional pronouns are not used in contemporary standard English unless a deliberate attempt is being made to imitate old-fashioned or biblical language in a humorous or literary context. Some of them have survived in certain dialects of English.

“Y’all” and other nonstandard second-person plural pronouns

The lack of distinction between singular and plural in the standard second-person pronouns has given rise to various ways of expressing the plural in different dialects.

Though some of these are very widely used in everyday speech, they are all still regarded as nonstandard and not used in formal or academic writing. Some examples are given in the table below.

“Ourself”

In some contexts, an individual might refer to themselves as “we” and therefore use the alternative reflexive pronoun ourself.

This commonly occurs with the royal we (used by monarchs), the editorial we (used by an individual speaking for a publication or organization), and the generic we (used to make generalizations). It’s not advisable to use any of these, or the word “ourself,” in academic writing.

“Hisself,” “theirselves,” and “theirself”

Other nonstandard reflexive pronouns are hisself (replacing “himself”), theirselves (replacing “themselves”), and theirself (replacing “themself”). These are all widely regarded as mistakes and should be avoided in writing generally, whether formal or informal.

’Em

The third-person plural object pronoun “them” is often replaced by ’em in informal contexts. It’s older than you’d expect, believed to actually be an abbreviation of the Middle English pronoun “hem” rather than the current pronoun “them.” But it’s not used in formal or academic writing.

Capitalized pronouns in a religious context

The second- and third-person pronouns He/Him/His/Himself, She/Her/Hers/Herself, and You/Yours/Yourself are sometimes capitalized in a religious context when they are used to refer to a deity.

This is commonly encountered in sacred works such as the Bible or the Quran and in the writing of other religious figures, though it’s not always done consistently. It’s not necessary to imitate this usage in a nonreligious context.

Frequently asked questions about personal pronouns

What is a personal pronoun?

Personal pronouns are words like “he,” “me,” and “yourselves” that refer to the person you’re addressing, to other people or things, or to yourself. Like other pronouns, they usually stand in for previously mentioned nouns (antecedents).

They are called “personal” not because they always refer to people (e.g., “it” doesn’t) but because they indicate grammatical person (first, second, or third person). Personal pronouns also change their forms based on number, gender, and grammatical role in a sentence.

What are the first, second, and third person?

In grammar, person is how we distinguish between the speaker or writer (first person), the person being addressed (second person), and any other people, objects, ideas, etc. referred to (third person).

Person is expressed through the different personal pronouns, such as “I” (first-person pronoun), “you” (second-person pronoun), and “they” (third-person pronoun). It also affects how verbs are conjugated, due to subject-verb agreement (e.g., “I am” vs. “you are”).

In fiction, a first-person narrative is one written directly from the perspective of the protagonist. A third-person narrative describes the protagonist from the perspective of a separate narrator. A second-person narrative (very rare) addresses the reader as if they were the protagonist.

What are preferred pronouns?

The term preferred pronouns is used to mean the personal pronouns a person identifies with and would like to be referred to by. People usually state the subject and object pronoun (e.g., “she/her”) but may also include the possessive (e.g., “she/her/hers”).

Most people go by the masculine “he/him,” the feminine “she/her,” the gender-neutral singular “they/them,” or some combination of these. There are also neopronouns used to express nonbinary gender identity, such as “xe/xem.” These are less common than the singular “they.”

The practice of stating one’s preferred pronouns (e.g., in a professional context or on a social media profile) is meant to promote inclusion for transgender and gender-non-conforming people.

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“Look-alike”

Look-alike is a noun referring to a person or thing that looks like someone or something else. It can also be used as an adjective when it occurs before a noun. The plural form of look-alike is look-alikes.

Note that only the noun is hyphenated; no hyphen should be added when using “look alike” as a verb: “They’re brothers, but they don’t look alike.”

“Great minds think alike”

Great minds think alike is a humorous expression used to acknowledge that two or more people share the same opinion or have reached the same conclusion at the same time. It’s typically used by one of the like-minded people to mean “we are very clever.”

“Share and share alike”

Share and share alike is an idiom used to emphasize that each member of a group should receive an equal share of something. It’s often used in a legal context, such as in a will, to indicate that each individual should receive the same amount.

Other interesting language articles

If you want to know more about commonly confused words, definitions, and differences between US and UK spellings, make sure to check out some of our other language articles with explanations, examples, and quizzes.

Frequently asked questions

What is a synonym for “alike”?

There are numerous synonyms and near-synonyms for the two meanings of alike.

Similar (adjective)	Similarly (adverb)
Identical	Correspondingly
Indistinguishable	Identically
Interchangeable	Likewise
Matching	The same
The same	Uniformly
Undifferentiated
Uniform

How do I use “alike” in a sentence?

Alike can be used as an adjective meaning “similar” (e.g., “we’re alike in our beliefs”) and an adverb meaning “in a similar way” (e.g., “the brothers think alike”).

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Generalizability is determined by how representative your sample is of the target population. This is known as external validity.

What is generalizability?

The goal of research is to produce knowledge that can be applied as widely as possible. However, since it usually isn’t possible to analyze every member of a population, researchers make do by analyzing a portion of it, making statements about that portion.

To be able to apply these statements to larger groups, researchers must ensure that the sample accurately resembles the broader population.

In other words, the sample and the population must share the characteristics relevant to the research being conducted. When this happens, the sample is considered representative, and by extension, the study’s results are considered generalizable.

In general, a study has good generalizability when the results apply to many different types of people or different situations. In contrast, if the results can only be applied to a subgroup of the population or in a very specific situation, the study has poor generalizability.

Why is generalizability important?

Obtaining a representative sample is crucial for probability sampling. In contrast, studies using non-probability sampling designs are more concerned with investigating a few cases in depth, rather than generalizing their findings. As such, generalizability is the main difference between probability and non-probability samples.

There are three factors that determine the generalizability of your study in a probability sampling design:

• The randomness of the sample, with each research unit (e.g., person, business, or organization in your population) having an equal chance of being selected.
• How representative the sample is of your population.
• The size of your sample, with larger samples more likely to yield statistically significant results.

Generalizability is one of the three criteria (along with validity and reliability) that researchers use to assess the quality of both quantitative and qualitative research. However, depending on the type of research, generalizability is interpreted and evaluated differently.

• In quantitative research, generalizability helps to make inferences about the population.
• In qualitative research, generalizability helps to compare the results to other results from similar situations.

Examples of generalizability

Generalizability is crucial for establishing the validity and reliability of your study. In most cases, a lack of generalizability significantly narrows down the scope of your research—i.e., to whom the results can be applied.

However, research results that cannot be generalized can still have value. It all depends on your research objectives.

Types of generalizability

There are two broad types of generalizability:

• Statistical generalizability, which applies to quantitative research
• Theoretical generalizability (also referred to as transferability), which applies to qualitative research

Statistical generalizability is critical for quantitative research. The goal of quantitative research is to develop general knowledge that applies to all the units of a population while studying only a subset of these units (sample). Statistical generalization is achieved when you study a sample that accurately mirrors characteristics of the population. The sample needs to be sufficiently large and unbiased.

In qualitative research, statistical generalizability is not relevant. This is because qualitative research is primarily concerned with obtaining insights on some aspect of human experience, rather than data with solid statistical basis. By studying individual cases, researchers will try to get results that they can extend to similar cases. This is known as theoretical generalizability or transferability.

How do you ensure generalizability in research?

In order to apply your findings on a larger scale, you should take the following steps to ensure your research has sufficient generalizability.

• Define your population in detail. By doing so, you will establish what it is that you intend to make generalizations about. For example, are you going to discuss students in general, or students on your campus?

• Use random sampling. If the sample is truly random (i.e., everyone in the population is equally likely to be chosen for the sample), then you can avoid sampling bias and ensure that the sample will be representative of the population.

• Consider the size of your sample. The sample size must be large enough to support the generalization being made. If the sample represents a smaller group within that population, then the conclusions have to be downsized in scope.

• If you’re conducting qualitative research, try to reach a saturation point of important themes and categories. This way, you will have sufficient information to account for all aspects of the phenomenon under study.

After completing your research, take a moment to reflect on the generalizability of your findings. What didn’t go as planned and could impact your generalizability? For example, selection biases such as non-response bias can affect your results. Explain how generalizable your results are, as well as possible limitations, in the discussion section of your research paper.

Frequently asked questions about generalizability

Why is generalizability important in research?

Generalizability is important because it allows researchers to make inferences for a large group of people, i.e., the target population, by only studying a part of it (the sample).

What is the difference between internal and external validity?

Internal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables.

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design.

What goes in the discussion chapter of a dissertation?

In the discussion, you explore the meaning and relevance of your research results, explaining how they fit with existing research and theory. Discuss:

• Your interpretations: what do the results tell us?
• The implications: why do the results matter?
• The limitations: what can’t the results tell us?

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To find out, we tested 10 of the most popular free grammar checkers, checking how many errors they could fix in our sample text and deducting points for any new errors introduced. We also assessed the tools’ user-friendliness. The results show a clear winner: QuillBot.

1. QuillBot review

QuillBot performed exceptionally well compared to the other tools we tested for this comparison. It was able to find and correct 19 of the 20 errors.

It did appear to notice the 20th error, but its attempt to fix it was not effective and introduced a new issue, so we deducted a point, resulting in a score of 18 out of 20. Despite this bug, that’s drastically higher than the second-highest scorer, Wordtune (13 out of 20).

We also found the QuillBot website to be very user-friendly. The “Fix All Errors” option saves a lot of time and clicking when a high number of corrections are needed. And getting started is very quick, since no sign-up is required and you can just copy text directly into the site.

Quality of check

• Noticed all 20 errors
• Correctly fixed 19 of the 20 errors

• For the final error, suggested lowercasing the word “And” but not removing the period before it, which is incorrect

Usability

• No sign-up required, so it’s very quick to get started
• “Fix All Errors” button makes it very easy to correct a lot of issues at once
• Errors can be fixed individually with one click
• There’s a shortcut to QuillBot’s paraphraser tool, if you want to use this too
• Various formatting options, an export button, and a copy full text button built in

Check out QuillBot’s grammar checker

2. GrammarCheck review

Though it performed a lot worse than QuillBot, GrammarCheck did better than the average free grammar checker. It corrected 11 out of 20 errors in the text and did not introduce any errors in the process.

In terms of usability, we found GrammarCheck straightforward to use, since there’s no sign-up required and you can just copy-paste text into the site. It also takes only one click to accept a change in the text.

But the interface on the site looks quite dated, and convenient options like a “Fix All Errors” button are missing. The “Deep Check” button seems to redirect the user to Grammarly, although GrammarCheck’s own tool does seem slightly different from Grammarly’s.

Quality of check

• Fixed 11 of 20 errors, the same number as Grammarly

• Ignored the other 9 errors (mostly issues with punctuation and sentence structure), suggesting it can only handle the basics

Usability

• No sign-up needed
• Quick and easy to use—just paste text in
• One click to accept a change

• Interface is quite old-fashioned
• No “Fix All Errors” button
• “Deep Check” just redirects you to Grammarly

Try GrammarCheck

3. Grammarly review

Grammarly performs well compared to the average grammar checker. But because it’s not entirely free, many of the issues it notices can only be checked if you pay for a premium membership. The free version of Grammarly could only fix 11 out of 20 errors in our text.

Based on the other parts of the text it had highlighted, it looks like premium version of Grammarly would be able to detect 15 of the 20 errors—notably, still a lower number than QuillBot detects and fixes for free.

In terms of usability, we found it somewhat inconvenient to have to sign up to use the grammar checker, but we appreciated the clean look of the interface and the fact that it gave multiple options for corrections when different interpretations were possible. However, we found the process of fixing errors slower than it needed to be.

Quality of check

• Fixed 11 of 20 errors

• Noticed some (though not all) other errors, but you have to pay for premium to see them
• Unnecessary suggestions like adding “the” before “chronic obstructive pulmonary disease”

Usability

• Offers a browser extension
• The interface has a nice, clean look
• Gives multiple options when different interpretations are possible

• No “Fix all errors” button
• Two clicks required to fix an error
• Need to sign up to use it at all
• Can’t see all suggestions at once, only one type at a time

Try Grammarly

4. Wordtune review

Wordtune performed better than any other grammar checker except QuillBot in terms of the errors it found and corrected, with a score of 13 out of 20. However, this is with the important caveat that it’s more of a paraphrasing tool than a standard grammar checker.

As far as we could tell, there was no way to separate its paraphrasing functionality from its basic grammar checking, so the only way it would fix most errors in the text was by completely rewriting sentences. This makes it an inappropriate choice if you want to keep the structure of your text intact.

We also found that the paraphrases offered would sometimes introduce new errors or significantly change your meaning. And we found it inconvenient to have to sign up before using the tool. For these reasons, we’ve ranked it lower than some other tools despite its higher score “on paper.”

Quality of check

• Fixed 13 out of 20 errors, more than most tools

• Some suggested paraphrases introduced new errors
• Some paraphrases significantly alter your meaning

Usability

• Offers a browser extension

• No way to isolate individual problems rather than rewriting whole sentences
• No “Fix All Errors” button
• Corrections require two clicks
• Have to sign up to use it

Try Wordtune

5. LanguageTool review

LanguageTool performed relatively poorly, detecting only 6 out of 20 errors in the text. For the most part, it was only able to recognize spelling mistakes and word choice errors, though it did notice one punctuation error.

It also labeled the word “apraxia” a “possible spelling mistake.” Since it gave the option of adding the word to a personal dictionary, we haven’t deducted a point for this. But we do think failure to recognize a word that appears in many widely used dictionaries is a bad sign.

We did find the site relatively user-friendly, since it doesn’t require a sign-up and has a decent user interface. But this isn’t enough to make up for its poor level of error detection.

Quality of check

• Only 6 out of 20 errors detected
• Unable to fix parentheses error directly—it pointed it out, but we had to fix it manually
• Didn’t recognize the word “apraxia”

Usability

• No sign-up required, quick and easy to get started
• Offers a browser extension
• Gives multiple options when different interpretations are possible

• Two clicks to correct an error
• No “Fix All Errors” button

Try LanguageTool

6. Writer review

Writer corrected 7 errors in the text, a slightly better performance than LanguageTool, but it also introduced 4 new errors in the process of correcting them, resulting in a score of 3 out of 20.

The errors it introduced were all punctuation errors. Writer seems to attempt to simplify your sentence structure in a clumsy way, by simply removing commas and parentheses that are grammatically necessary. This results in confusing and grammatically incorrect sentences.

The interface on the Writer site is fairly clean and usable, and it does allow you to accept each change with one click. But you have to go through a sign-up process before you can start using it. And due to the various errors introduced, it’s hard to recommend Writer.

Quality of check

• Only fixed 7 out of 20 errors …
• … and introduced 4 new errors in the process

Usability

• Offers a browser extension
• One-click corrections
• Fairly clean and usable interface

• Sign-up required before you can start
• Many basic corrections require a premium membership
• No “Fix All Errors” button
• Labeling of types of errors seems misleading (e.g., punctuation issues labeled “Spelling”)

Try Writer

7. ProWritingAid review

Like Writer, ProWritingAid was able to find only a few errors and introduced some errors of its own. In this case, it fixed 5 errors and introduced 2, resulting in a score of 3 out of 20.

The errors introduced by ProWritingAid resulted from identifying issues but fixing them in ways that didn’t work. For example, it identified that the acronym “C.O.P.D” is not correct, but suggested the solution “C.O.P. D” with a space inserted before the “D”—which is just as bad.

We found the interface only moderately user-friendly. The interface of the tool looks fairly appealing, but it’s also very busy, with a lot of unnecessary information and options displayed in a distracting way. It also can’t be used without first signing up for an account.

Quality of check

• Only fixed 5 out of 20 errors …
• … and introduced 2 new errors in the process

Usability

• Offers an extension
• Interface has a fairly nice look to it
• One-click corrections
• Gives multiple options when different interpretations are possible

• Sign-up required
• Somewhat confusing site design makes it hard to find the tool itself
• Interface is quite overwhelming and distracting
• No “Fix All Errors” button

Try ProWritingAid

8. Linguix review

Linguix had the worst performance out of any of the tools we tested, fixing only 3 errors and introducing 1 new error, resulting in a score of 2 out of 20. It highlighted 7 other errors, but as with some other tools, it would only explain and correct them in the premium version.

Like LanguageTool, it was unable to recognize the word “apraxia.” In this case, we’ve deducted a point for this, since it simply labels the word as a “spelling mistake” without any suggestion it could be misleading the user.

It also wasn’t all that user-friendly. While the interface looks nice enough and one-click corrections are possible, you have to sign up to edit texts above a certain length. And since even the premium version seems to find less than half the errors in the text, we can’t recommend it.

Quality of check

• Fixed only 3 out of 20 errors …
• … and introduced 1 new error

Usability

• Decent-looking interface
• One-click corrections

• Almost all corrections gated behind premium membership
• No “Fix All Errors”
• Need to sign up to edit texts of this length

Try Linguix

9. Scribens review

Scribens (not to be confused with Scribbr!) scored the same as ProWritingAid, with 5 errors fixed and 2 new ones introduced, resulting in a score of 3 out of 20. In one case, it suggested changing “multiple sclerosis” to “many sclerosis,” quite a bad mistake.

Scribens also offers a variety of extra features that can be turned on at the side. We found that these features introduced various major errors into the text when turned on, such as changing “they’re” into “they’represent.” The one that claimed to detect run-on sentences actually appeared to just highlight every long sentence.

Since these features were turned off by default, we haven’t deducted points for them, but we strongly recommend against using them. In terms of user interface, Scribens is at least easy to get started with, but it’s quite dated in appearance and performs very poorly overall.

Quality of check

• Only fixed 5 out of 20 errors …
• … and introduced 2 new errors
• Optional features in the sidebar mostly create nonsense text

Usability

• No sign-up, easy to get started

• Dated interface
• Two clicks to fix an error
• No “Fix All Errors” button

Try Scribens

10. Ginger review

Ginger scored the same as several other tools we tested, finding only 3 out of 20 errors but not adding any errors of its own. We’ve ranked it lowest out of all the tools we tested because of some unreasonable limitations regarding text length.

Ginger claims that its grammar checker will only “partially” correct sentences of more than 350 characters. In practice, we found that it just ignored these sentences entirely, making it useless for checking any kind of academic text, where long sentences are common.

It also wouldn’t accept texts longer than 900 characters (450 if you don’t sign up), meaning we had to check the two paragraphs of our text separately. On the whole, we found the Ginger tool to be exceptionally poor and impractical.

Quality of check

• Only fixed 3 out of 20 errors

Usability

• Offers an extension
• Auto-inputs corrections

• Sign-up needed for 450+ characters, premium for 900+
• Refuses to correct sentences of 350+ characters at all!
• Interface is very cluttered

Research methodology

To compare the capabilities of different grammar checkers objectively, we used the same text for all tests and applied the same criteria to our assessment of the results.

Text used for testing

To test each grammar checker, we used the same 160-word text, shown below. It contains 20 grammatical, spelling, word choice, or punctuation errors. You can see a description of each error by mousing over them in the text.

Assessment criteria

We pasted the text above into each of the grammar checkers and assessed how effectively they were able to correct the errors. Each checker received a score out of 20 based on the following rules:

• +1 point for each mistake that was corrected
• −1 point for each new mistake introduced

For example, if the tool “fixed” an existing error but introduced a new error in the process, it would gain no points for that error. If it introduced an error without fixing the existing one, it would lose a point.

The usability of each tool was assessed qualitatively based on factors like how quickly the errors could be corrected, whether a sign-up was required, how clear and user-friendly the interface was, and whether the tool could check the entire text at once.

Frequently asked questions about grammar checkers

What is the best free grammar checker?

Our research indicates that the best free grammar checker available online is the QuillBot grammar checker.

We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.

It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.

Why should I use a grammar checker?

Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing. Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.

If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation, word choice, and sentence structure that more basic tools would miss.

Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.

What’s the difference between a grammar checker and a paraphrasing tool?

A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes, and problems with sentence structure. You can check out our analysis of the best free grammar checkers to learn more.

A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.

Some tools available online combine both functions. Others, such as QuillBot, have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.

The post 10 Best Free Grammar Checkers | Tested & Reviewed appeared first on Scribbr.

What is survivorship bias?

Survivorship bias is a form of selection bias. It occurs when a dataset only considers existing (or “surviving”) observations and fails to consider observations that have ceased to exist.

For example, when investigating the profitability of the tech industry, one has to also study businesses that went bankrupt, rather than only focusing on businesses currently in the market.

Focusing on a subset of your sample that has already passed some sort of selection process increases your chances of drawing incorrect conclusions. “Surviving” observations may have survived exactly because they are more resilient to difficult conditions, while others have ceased to exist as a result of those same conditions.

Why survivorship bias matters

When a study is affected by survivorship bias, we only pay attention to part of the data. This can have a number of consequences, such as:

1. Overly optimistic conclusions that do not represent reality, leading us to think that circumstances are easier or more likely to work in our favor than they actually are. Studying only successful entrepreneurs or startups can have this effect.
2. Misinterpretation of correlation, or seeing a cause-and-effect relationship where there isn’t one. High school or college dropouts, for example, become successful entrepreneurs despite leaving school, not because of it.
3. Incomplete decision-making, missing out on important information about those who didn’t “survive,” such as businesses that failed despite seemingly fertile growth environments or hard-working founders.

Awareness of survivorship bias is important because it impacts our perception, judgment, and the quality of our conclusions.

Survivorship bias examples

Survivorship bias can cloud our judgment not only in research, but in everyday life, too.

Relatedly, before drawing any conclusions, you need to ask yourself whether your dataset is truly complete. Otherwise, you are also at risk of survivorship bias.

How to prevent survivorship bias

Survivorship bias is a common logical error, but you can take several steps to avoid it:

• Consider what’s missing from your data, asking yourself: Is your dataset complete? What observations didn’t “survive” from an event or selection procedure? Think of defunct businesses, school dropouts who didn’t become billionaires, or clinical trial participants who showed no improvement.
• Pick data sources crafted to ensure accuracy. Be careful not to omit observations that no longer exist and would change your analysis or conclusions. For example, also look for academic journals publishing negative study results.
• When cleaning your data, be mindful of outlier removal. You may accidentally remove critical information if you don’t understand what the outliers mean. It’s crucial to determine whether any extreme values are truly errors or instead represent something else. Experts on the subject matter of your research can help you with this, as they know the field best.

Being aware of survivorship bias, as well as being open and transparent about your assumptions, is the best strategy to prevent it.

Frequently asked questions about survivorship bias

What are common types of selection bias?

Common types of selection bias are:

• Sampling or ascertainment bias
• Attrition bias
• Volunteer or self-selection bias
• Survivorship bias
• Non-response bias
• Undercoverage bias

Why is bias in research a problem?

Bias affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where new forms of treatment are being evaluated.

What’s the difference between reliability and validity?

Reliability and validity are both about how well a method measures something:

• Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

The post What Is Survivorship Bias? | Definition & Examples appeared first on Scribbr.