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Perception bias is the tendency to perceive ourselves and our environment in a subjective way. Although we like to think our judgment is impartial, we are, in fact, unconsciously influenced by our assumptions and expectations.

Example: Perception biasAfter a few weeks at your new job, you notice that some of your colleagues always go for after-work drinks on Fridays. It’s not an official team event, but each week the same person asks who’s joining and books a table. However, no one ever asks the older colleagues to join, assuming that they won’t be interested.

If left unchecked, perception bias can affect how we evaluate ourselves and others. As a result, we may form inaccurate impressions.This, in turn, can impact the quality of our decision-making.

What is perception bias?

Perception bias is a broad term used to describe different situations in which we perceive inaccuracies in our environment. It is a type of cognitive bias that occurs when we subconsciously form assumptions or draw conclusions based on our beliefs, expectations, or emotions.

Perception bias works like a filter, helping us make sense of all the information we are exposed to in our surroundings. As a result, our perception of reality is often distorted. For example, this can cause us to unfairly label people or make inferences about their abilities on the basis of superficial observations or stereotypes.

Why does perception bias occur?

Perception bias occurs because our perception is selective. Here, perception refers to the process of screening, selecting, organizing, and interpreting stimuli, such as words or objects, in order to give them meaning. Our brain chooses to hone in on one or very few stimuli out of the multitude of stimuli surrounding us. This is one way our brains differentiate between important and unimportant things.

Due to this, our perception of a given situation is not a photographic representation of reality. Rather, it is a unique representation, informed by objective information, our prior beliefs and expectations (called cognitive factors), and our hopes, desires, and emotions (called motivational factors). Motivational and cognitive factors are sometimes intertwined, but they can also function separately.

What are different types of perception bias?

There are many types of bias that can influence our perceptions, whether of objects, others, or ourselves. Although there is no exhaustive list, the following are some common types of perception bias:

Visual Perception. When we look at something, our brains use the information available (like visual cues or prior experience) to make sense of an object. This means that our visual processing of faces can be biased. For example, a person’s group membership may lead us to view their face as untrustworthy. Negative attitudes and beliefs like outgroup bias can have an effect on our visual perception.

Self-perception. People are often biased in their self-perceptions, failing to assess themselves accurately. For example, people may take personal responsibility for successes while denying personal responsibility for failures (self-serving bias), or they may underestimate their performance and abilities, casting themselves in a more negative light (self-effacement bias). When comparing the self to others, people often commit what is known as the false consensus effect, believing that our opinions or behaviors are generalizable to the general population.

Perception bias examples

Example: Perception bias in the workplaceYou are the lead for an important project at work, and your manager asks you to present your progress to the executive board. You spend hours preparing the presentation with your team. After the presentation, your manager congratulates you for your progress and the presentation. In reply, you say “I worked really hard on this,” happy to take all the credit. Since you are the project lead, you believe this praise is fair. However, this is not entirely accurate because you worked as a team. This is an example of a type of perception bias called self-enhancement.

Selective perception bias can help explain why individuals with opposing views tend to find the same media coverage to be biased against them.

Example: Selective perception bias and the “hostile media effect”In one study, researchers took a sample of pro-Israeli, pro-Arab, and neutral college students. They asked them to watch the same set of televised news segments covering the Arab-Israeli conflict, broadcast nationally in the United States over a ten-day period.

Researchers found that each side saw the news coverage as biased in favor of the other side.

Pro-Arab students thought the news segments were generally biased in favor of Israel.

Pro-Israeli students thought the segments were generally biased against Israel.

Neutral students gave opinions that fell between the two groups.

They also found that these disagreements were not simply differences of opinion; they were differences in perception. In particular, pro-Arab and pro-Israeli students also differed in their perceptions of the number of favorable and unfavorable references that had been made about Israel during the news program.

Pro-Arab students reported that 42 percent of the references to Israel had been favorable, and only 26 percent had been unfavorable.

Pro-Israeli students recalled 57 percent of the references to Israel as having been unfavorable and only 16 percent as having been favorable.

The researchers concluded that individuals with strong preexisting attitudes on an issue perceive media coverage as unfairly biased against their side and in favor of their opponents’ point of view. This happens because when people become committed to a particular cause or opinion, their perceptions often change in order to remain consistent with this commitment.

How to reduce perception bias

Although it is not possible to entirely eliminate perception bias, there are ways to reduce it. More specifically, when you make a decision or form an impression of someone, you can ask yourself the following questions:

Do I have a motive that makes me see things a certain way?

What are my expectations from this situation or decision?

Have I discussed my thoughts or opinions with people who don’t agree with me?

If you find yourself making absolute statements about others, using strong words like “always” or “all,” ask yourself how accurate this is, and whether you have evidence to back up your claim.

Other types of research bias Frequently asked questions about perception bias

What is an everyday life example of perception bias?

A real-life example of perception bias is the false consensus effect. Because we spend most of our time with friends, family, and colleagues who share the same opinions or values we do, we are often misled to believe that the majority of people think or act in ways similar to us. This explains, for instance, why some people take office supplies home: they may genuinely feel that this behavior is more common than it really is.

Why is perception bias a problem?

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

What is selective perception?

Selective perception is the unconscious process by which people screen, select, and notice objects in their environment. During this process, information tends to be selectively perceived in ways that align with existing attitudes, beliefs, and goals.

Although this allows us to concentrate only on the information that is relevant for us at present, it can also lead to perception bias. For example, while driving, if you become hyper-focused on reaching your exit on a highway, your brain may filter visual stimuli so that you can only focus on things you need to notice in order to exit the highway. However, this can also cause you to miss other things happening around you on the road.

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What Is Optimism Bias?

Optimism bias is the tendency to overestimate the likelihood of positive events and underestimate the likelihood of negative events. Optimism bias causes most people to expect that things will work out well, even if rationality suggests that problems are inevitable in life.

Example: Optimism biasYou’ve just bought a new bike, and the salesperson asks you whether you also want to look for a helmet.

Because you’ve been riding a bike since you were young, you think the chances of getting involved in an accident are really small. You conclude that you’ll be fine without it. Optimism bias makes you underestimate the risk of riding a bike without a helmet.

Although optimism bias can motivate us to overcome obstacles, it can also cause us to ignore potential risks, resulting in poor decision-making.

What is optimism bias?

Optimism bias (or unrealistic optimism) is a type of unconscious cognitive bias. It refers to an unrealistically favorable attitude that people have towards themselves and people that are close to them. Positive illusions help us maintain self-esteem and avoid discomfort, at least in the short term.

Optimism bias causes people to believe that they are less likely to experience negative events than other people. For example, people expect that their careers, marriages, or health will be better than those of others, and that the financial troubles, divorces, or illnesses that happen to other people will not happen to them.

This irrational belief seems to be deeply ingrained in humans. Studies suggest that it is observed in about 80% of the population (but, notably, not among people with depression).

Why does optimism bias occur?

Throughout human evolution this characteristic served us well and was passed down from one generation to the next. In other words, because optimism bias proved beneficial to humans, we are inclined to mispredict the future.

There are two key assumptions at the root of optimism bias:

That we exercise some level of control over the world around us, including what will happen to us in the future.

That we, as individuals, possess more positive traits than the average person.

Several factors can help explain optimism bias:

We have the tendency to selectively update our beliefs and expectations about the future. We are more likely to update our beliefs based on positive information rather than negative information. This, in turn, perpetuates optimism bias.

Optimism is beneficial to our mental and physical health. Expecting positive outcomes reduces stress and anxiety. Optimistic patients are more likely to believe that they will recover, leading them to adopt behaviours that increase their chances (e.g., exercise, healthy diet).

Overall, optimism bias enables us to cope with our environment and worry less about uncertainty. Because of this, it can often lead to better results than unbiased or rational beliefs.

Why does optimism bias matter?

Because a majority of people are susceptible to optimism bias, it’s important to be aware of its influence on our perception and judgment.

Optimism bias can be a problem when it prevents us from accurately anticipating risk. In project management, for instance, optimism bias can cause us to underestimate the budget and time needed, a common error called the planning fallacy. Failure to assess potential hazards can also mean failing to take out sufficient insurance or to get regular medical check-ups. It can even cause us to adopt harmful habits, such as smoking.

On the other hand, optimism is also linked to achievement in several domains, such as sports, business, and education. When we are optimistic, we are more motivated to try harder, which in turn can influence the outcome. Sometimes, expecting positive things can become a self-fulfilling prophecy.

Optimism bias examples

Optimism bias can also influence collective behaviour and produce large-scale effects.

Example: Optimism bias and the economySeveral experts consider optimism bias to be one of the core causes of the financial crisis of 2007-2008. Individuals, analysts, and government officials were all too optimistic that the economy would grow (i.e., that businesses would continue to be profitable, that there would be more jobs for people, and that incomes would increase), leading them to ignore any warning signs.

This shows that when many people hold unrealistic expectations, their bias accumulates and is amplified, producing large scale effects.

Optimism bias can have negative consequences, particularly when serious risks are disregarded.

Example: Optimism bias and climate changeSome argue that optimism bias may help explain why we don’t do anything about climate change, even though we acknowledge the threat. Studies have shown that people who are more optimistic about a range of possible future events (e.g., contracting an illness, World War III) are also less concerned about the environment.

Additionally, among climate skeptics in particular, more optimism is associated with less guilt, less perceived responsibility, and lower behavioural intentions. Thus, overall, optimism seems to be negatively associated with an active response to environmental change.

How to avoid optimism bias

Although optimism bias is part of human nature (and can’t be entirely avoided), there are ways to keep it in check:

Perform a project “premortem.” A premortem analysis starts with the hypothesis that your project has failed. With that in mind, you try to come up with possible reasons why. This allows you to spot the weaknesses in your project plan and prepare for the future.

Use the availability heuristic. Actively attempt to retrieve negative past experiences or times things didn’t go as planned. Here, the purpose is not to demotivate yourself, but to learn from the past so as to make sensible choices in the future.

Take an outsider’s approach. Take an objective approach when making plans. For example, when you estimate how long you will need to write a paper, seek out information about the average time it takes most people and adjust your initial assumptions accordingly.

Other types of research bias Frequently asked questions about optimism bias

What is the opposite of optimism bias?

The opposite of optimism bias is pessimism bias. Optimism bias occurs when we overestimate our chances of experiencing positive events in our lives, while pessimism bias occurs when we overestimate our chance of experiencing negative events.

For example, pessimism bias could cause someone to think they are going to fail an exam, even though they are well prepared and usually get good grades.

What is a positive illusion?

A positive illusion is a form of self-deception under which people have inflated, favorable attitudes about themselves or others close to them.

The most common positive illusions involve:

Exaggerating one’s positive traits

Overestimating one’s degree of control in life

Harboring overly optimistic beliefs about future events (also called optimism bias).

What is the planning fallacy?

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias.

What is a self-fulfilling prophecy?

This suggests that beliefs have the power to alter people’s behavior in such a way that they become a new reality in the end. Optimism bias can create a self-fulfilling prophecy: when we expect positive things, we are more likely to align our actions with this belief and try harder to influence the outcome.

What is positivity bias?

Positivity bias occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias, or the expectation that things will work out well, even if rationality suggests that problems are inevitable in life.

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Fighting Data Bias – Everyone’s Responsibility

This article was published as a part of the Data Science Blogathon

What is Bias?

PiCTURE CREDIT: PHIL BRAY/NETFLIX

We all know that society is biased for ages. Bias based on gender, race, age, socioeconomic status, etc have consciously or unconsciously been part of human thoughts and actions. In this modern society with the help of raising awareness, most of us are coming forward to fight against discrimination and prejudice that is affecting human decision-making.

But what about the decision-making done by intelligent systems and applications that are increasingly becoming an inevitable part of our lives?

These intelligent applications are built on data supplied by humans. When the bias is present in human thoughts and actions, there is no surprise that the intelligent applications that we are developing are inheriting this bias from us.

What is Data Bias?

Consider an NLP application fills a sentence as ‘Father is to doctor as a mother is to nurse’.

The above NLP example is directly linked to the gender inequality present in society.

Consider more examples:

Why did one of the popular AI-based recruitment software biased against women applicants?

Why did Siri and Alexa show gender bias initially?

There are many reports that a lot of image processing applications fail to recognize women, especially dark-skinned women.

Why did the AI-based decision support application fail to identify criminals belonging to a particular race?

Why is this bias in the output given by these ML/AI applications?

Because the Machine Learning / AI applications that we design learn from the data that we feed to them. The data we feed contains the prejudices and inequalities that exist in the human world consciously or unconsciously.

  How serious are the implications of neglecting bias in the data?

As Data Scientists/ Data Analysts/Machine Learning Engineers and AI practitioners, we know that if our data sample does not represent the whole population, then our results are not statistically significant. Which means that we do not get accurate results.

Machine Learning models built on such data would perform worse on underrepresented data.

Consider an example from one of the critical domains, Healthcare, where data bias would result in devastating results.

The AI algorithms developed to detect skin cancer as perfectly as an experienced dermatologist failed to detect skin cancers in people with dark skin. Refer to the picture shown above.

Why did this happen?

Because the dataset was imbalanced. Majority of the images on which the algorithms were trained belong to light-skinned individuals. The data that was used to train these algorithms was taken from those states where the majority are white-skinned people. Hence, the algorithm fails to detect the disease in dark-skinned people when the images belonging to them were given to it.

Another AI application developed to identify the early stages of Alzheimer’s disease in people took auditory tests from people. It takes the way a person speaks as input and analyzes that data to identify the disease. But as the algorithm was trained on the data from Native English speakers, when a non-native English speaker takes the test, it wrongly identified the pauses and mispronunciations as indicators of the disease. (An example of false positive)

What are the consequences of this wrong diagnosis in the above two examples? Where in the development process have we gone wrong? How can AI bias occur?

There are multiple factors behind these AI biases. There is no single root cause.

1. Missing diverse demographic categories.

Sampling errors are also majorly the result of improper data collection methods.

Datasets that do not include diverse demographic categories will be imbalanced/skewed and there are higher chances of overlooking these factors during the data cleaning phase.

2. Bias inherited from humans.

As discussed above, bias can be induced into data while labeling, most of the time unintentionally, by humans in supervised learning. This can be due to the fact that unconscious bias is present in humans. As this data teaches and trains the AI algorithm on how to analyze and give predictions, the output will have anomalies.

3. During the feature engineering phase

During the feature engineering phase, bias can occur.

For example, while developing an ML application for predicting loan approval, if features like race, gender are considered, these features would induce bias.

On the contrary, while developing an AI application for healthcare, if the same features like race, gender are removed from the dataset, this would result in the errors explained in the healthcare examples above.

Research on handling AI Bias

AI is being used widely in not only the popular domains but also in very sensitive domains like health care, criminal justice, etc. Hence, the debate on biased data and fairness in the output is always on in data and AI communities.

There is so much research and study going on to identify how bias is induced into the AI systems and how to handle it to reduce errors. Responsible AI and ethical AI are also been adopted widely to tackle the problem of bias too along with other AI challenges.

Are we not responsible to reduce this data bias?

One of the primary goals of using AI in decision support systems should be to make decisions less biased than humans.

Should we leave this biased-data problem to the researchers and carry on with our regular data cleaning tasks and trying to improve the accuracy of our algorithms as part of our development work?

As Artificial Intelligence is growing deeper and deeper into our lives, bias in data that is used for developing these applications can have serious implications not only on human life but also on the entire planet.

Hence, it is everyone’s responsibility to work towards identifying and handling bias at the early stages of development.

What is our part to reduce data bias?

Every data Machine Learning engineer/AI practitioner has to take the responsibility of identifying and removing bias while he works on developing artificially intelligent applications.

Here are some of the steps we can consider to take this forward.

We should not blindly build, develop applications with whatever data is available to us.

We need to work with researchers too and ensure that diverse data is available for our model development.

We have to be careful during the data collection phase to gain enough domain knowledge on the problem we are working on to be able to assess if the data collected includes diverse factors and has any chances of bias.

During the feature engineering phase, we should study the features in-depth combined with more research on the problem domain we are working in, to eliminate any features that may possibly induce bias.

Explainable AI and Interpretable AI also helps us to build trust in algorithms by ensuring fairness, inclusivity, transparency, and reliability.

Testing and evaluating the models carefully by measuring accuracy levels for different demographic categories and sensitive groups may also help in reducing algorithmic bias.

About Me:

Prasuna Atreyapurapu:

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Related

Test Bias With Minority Group

Psychologists use testing and assessment in a variety of contexts for a variety of purposes, including but not limited to job placement, diagnosing psychological disorders for mental health treatment, verifying health insurance coverage, conducting focus groups for market research, informing legal decisions and government policies, and developing measures to reliably assess personality characteristics.

The American Psychological Association’s (APA) Ethical Principles of Psychologists and Code of Conduct (2002) and the Standards for Education and Psychological Testing give guidelines for the ethical conduct of psychological testing and evaluation with racial/ethnic minorities.

Test Bias Types of Test Biases

following are the general categories of test bias

Construct- Validity Bias − This refers to whether a test directly measures what it was designed to measure. On an intelligence test, for illustration, scholars who are learning English will probably encounter words they have yet to learn. Accordingly, test results may reflect their fairly weak English- language chops rather than their academic or intellectual capacities.

Content- Validity Bias − This bias occurs when the content of a test is comparatively more delicate for one group of scholars than for others. For example, it can do when members of a pupil group, similar to various young groups, have not been given the same occasion to learn the material being tested, when scoring is illegal to a group (for illustration, the answers that would make sense in one group’s culture are supposed incorrect), or when questions are articulated in ways that are strange to certain scholars because of verbal or artistic differences. Item-selection bias, a subcategory of this bias, refers to individual test particulars more suited to one group’s language and artistic behaviors.

Test Bias with a Minority Group

The assessment process involving ethnical minorities has numerous avenues by which bias can crop. The impulses can do because of differences in culture or race and minority group status. Although culture has been defined in numerous ways, it generally refers to the behavior patterns, symbols, institutions, values, and mortal products of a society. On the other hand, race can be used to describe a racial, public, or artistic group. One’s race generally conveys a social-cerebral sense of “peoplehood” in which group members share a social and artistic heritage transmitted from one generation to another.

Moreover, racial group members frequently feel an interdependence of fate with others in the group. In addition to culture and race, members of racial minority groups also witness minority group status that involves a history of race or racial relations. This history has affected interpersonal relations, prospects, and performances. Therefore, to completely understand racial minority groups, their responses, and the assessment process, culture, race, and minority group status must be anatomized. Concern with test and dimension bias is not simply a matter of being “politically correct” or eternalized by ethnics disgruntled by their issues on colorful tests and measures. Bias does live in numerous of our assessment instruments and procedures.

Cultural bias in testing occurs if an assessment unfairly measures scholars’ skills and knowledge without considering scholars’ understanding of artistic traditions. When assessments do not consider scholars’ artistic differences, they fail to measure scholars’ capacities directly and can lead to opinions grounded on inaccurate data. Cultural bias in testing can occur when the annotator or the testing accouterments do not consider scholars’ lack of knowledge of semantics and experiences within a particular artistic group.

The Impact of cultural bias in testing is that a disproportionate number of scholars from minority artistic backgrounds have appertained to special education services. Also, when measuring proficiency in a language, scholars can be inaptly labeled as impaired because the test results indicate a language impairment. Still, the distinction in data may be due to artistic differences. The main specific of artistic test bias is that the tests are made up of a homogenous group of people who do not represent the cultural diversity of the scholars who take the test. In addition, the test itself could be culturally prejudiced because of the content of test particulars, the formatting of the test, or the terrain in which the assessment is being given.

One effect of cultural bias in testing is maintaining ethical conceptions by unfairly representing data as a suggestion of intelligence or capability. As a result, testing results unfairly measure scholars of color, scoring lower when the fault lies in prejudiced testing, not furnishing accurate measures of scholars’ capacities. As a result, scholars of color are placed in special education programs at a disproportionate rate. Likewise, prejudiced standardized testing perpetuates misconceptions about marginalized people and good academic achievement prospects.

Steps to Reduce Test Biases

Given that test results continue to be extensively used when making important opinions about scholars, test inventors and experts have linked several strategies that can reduce, if not exclude, test bias and unfairness. Many representative exemplifications include

Seeking diversity in the test- development staffing and training test inventors and songwriters to be apprehensive of the eventuality of artistic, verbal, and socioeconomic bias.

Having test accouterments reviewed by experts trained in relating artistic bias and by representatives of culturally and linguistically different groups.

Ensuring that norming processes and sample sizes used to develop norm- substantiated tests are inclusive of different pupil groups and large enough to constitute a representative sample.

Barring particulars that produce the largest racial and cultural performance gaps and opting for particulars that produce the lowest gaps — a fashion known as “the golden rule.” (This particular strategy may be logistically delicate to achieve, still, given the number of racial, ethnical, and artistic groups that may be represented in any given testing population).

Webbing for and barring particulars, references, and terms more likely to be obnoxious to certain groups.

Rephrasing tests into a test taker’s native language or using practitioners to restate test particulars.

Including further “performance-grounded” particulars to limit the part language and word choice play in test performance.

Using multiple assessment measures to determine academic achievement and progress and avoiding using test scores to reject other information to make important opinions about scholars.

Conclusion

Despite Its character being a scientific and precise tool for dimension, cerebral testing is a culturally prejudiced procedure that results in differentiation against minority groups, particularly against minority scholars. Academic achievement and intelligence tests, the two types of tests most constantly used in public seminaries, assume that all people have the same behaviors tapped by the questions on the tests. They also presume that there is uniformity of academy classes in this country and that all who take the tests have the same installation with the English language. This cultural bias is compounded by other factors, similar to the item selection process, the content of the particulars, and the responses considered respectable to those particulars.

What Is Critical Thinking?

Critical thinking is the ability to effectively analyze information and form a judgment.

To think critically, you must be aware of your own biases and assumptions when encountering information, and apply consistent standards when evaluating sources.

Critical thinking skills help you to:

Identify credible sources

Evaluate and respond to arguments

Assess alternative viewpoints

Test hypotheses against relevant criteria

Why is critical thinking important?

Critical thinking is important for making judgments about sources of information and forming your own arguments. It emphasizes a rational, objective, and self-aware approach that can help you to identify credible sources and strengthen your conclusions.

Critical thinking is important in all disciplines and throughout all stages of the research process. The types of evidence used in the sciences and in the humanities may differ, but critical thinking skills are relevant to both.

In academic writing, critical thinking can help you to determine whether a source:

Is free from research bias

Provides evidence to support its research findings

Considers alternative viewpoints

Outside of academia, critical thinking goes hand in hand with information literacy to help you form opinions rationally and engage independently and critically with popular media.

Critical thinking examples

Critical thinking can help you to identify reliable sources of information that you can cite in your research paper. It can also guide your own research methods and inform your own arguments.

Outside of academia, critical thinking can help you to be aware of both your own and others’ biases and assumptions.

Academic examples

Example: Good critical thinking in an academic contextYou’re writing a research paper on recent innovations in diabetes treatments. You read an article that claims positive results for an at-home treatment that was recently developed. The results of the research are impressive, and the treatment seems to be groundbreaking.

However, when you compare the findings of the study with other current research, you determine that the results seem improbable. You analyze the paper again, consulting the sources it cites.

You notice that the research was funded by the pharmaceutical company that created the treatment. Because of this, you view its results skeptically and determine that more independent research is necessary to confirm or refute them.

Example: Poor critical thinking in an academic contextYou’re researching a paper on the impact wireless technology has had on developing countries that previously did not have large-scale communications infrastructure. You read an article that seems to confirm your hypothesis: the impact is mainly positive. Rather than evaluating the research methodology, you accept the findings uncritically.

In this instance, you have failed to engage with the source critically and have displayed confirmation bias in accepting its conclusions based on a belief you already held.

Nonacademic examples

Example: Good critical thinking in a nonacademic contextYou are thinking about upgrading the security features of your home. You want to install an alarm system but are unsure what brand is the most reliable. You search home improvement websites and find a five-star review article of an alarm system. The review is positive. The alarm seems easy to install and reliable.

However, you decide to compare this review article with consumer reviews on a different site. You find that these reviews are not as positive. Some customers have had problems installing the alarm, and some have noted that it activates for no apparent reason.

Example: Poor critical thinking in a nonacademic contextYou support a candidate in an upcoming election. You visit an online news site affiliated with their political party and read an article that criticizes their opponent. The article claims that the opponent is inexperienced in politics. You accept this without evidence, because it fits your preconceptions about the opponent.

In this case, you failed to look critically at the claims of the article and check whether they were backed up with evidence because you were already inclined to believe them.

Prevent plagiarism. Run a free check.

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How to think critically

There is no single way to think critically. How you engage with information will depend on the type of source you’re using and the information you need.

However, you can engage with sources in a systematic and critical way by asking certain questions when you encounter information. Like the CRAAP test, these questions focus on the currency, relevance, authority, accuracy, and purpose of a source of information.

When encountering information, ask:

Who is the author? Are they an expert in their field?

What do they say? Is their argument clear? Can you summarize it?

When did they say this? Is the source current?

Where is the information published? Is it an academic article? Is it peer-reviewed?

Why did the author publish it? What is their motivation?

How do they make their argument? Is it backed up by evidence? Does it rely on opinion, speculation, or appeals to emotion? Do they address alternative arguments?

Critical thinking also involves being aware of your own biases, not only those of others. When you make an argument or draw your own conclusions, you can ask similar questions about your own writing:

Am I only considering evidence that supports my preconceptions?

Is my argument expressed clearly and backed up with credible sources?

Would I be convinced by this argument coming from someone else?

Other interesting articles

If you want to know more about ChatGPT, AI tools, citation, and plagiarism, make sure to check out some of our other articles with explanations and examples.

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What Is An Interjection?

An interjection is a word or phrase used to express a feeling or to request or demand something. While interjections are a part of speech, they are not grammatically connected to other parts of a sentence.

Interjections are common in everyday speech and informal writing. While some interjections such as “well” and “indeed” are acceptable in formal conversation, it’s best to avoid interjections in formal or academic writing.

Examples: Interjections in a sentenceWow! That bird is huge.

Uh-oh. I forgot to get gas.

We’re not lost. We just need to go, um, this way.

Psst, what’s the answer to number four?

How are interjections used in sentences?

Interjections add meaning to a sentence or context by expressing a feeling, making a demand, or emphasizing a thought.

Interjections can be either a single word or a phrase, and they can be used on their own or as part of a sentence.

Examples: Uses of interjections Phew!

Shoot, I’ve broken a nail.

Oh really? I didn’t know that.

As interjections are a grammatically independent part of speech, they can often be excluded from a sentence without impacting its meaning.

Examples: Sentences with and without interjections

Oh boy, I’m tired.

I’m tired.

Ouch! That hurts!

That hurts!

Primary interjections

A primary interjection is a word or sound that can only be used as an interjection. Primary interjections do not have alternative meanings and can’t function as another part of speech (i.e., noun, verb, or adjective).

Primary interjections are typically just sounds without a clear etymology. As such, while they sometimes have standard spellings, a single interjection may be written in different ways (e.g., “um-hum” or “mm-hmm”).

Examples: Primary interjections in a sentenceUgh! That’s disgusting.

Um-hum. I think that could work.

We won the game. Yippee!

Secondary interjections

A secondary interjection is a word that is typically used as another part of speech (such as a noun, verb, or adjective) that can also be used as an interjection.

Examples: Secondary interjections in a sentence

Goodness

! That was a close one.

Shoot! My flight has been canceled.

Awesome! Do that trick again.

Volitive interjections

A volitive interjection is used to give a command or make a request. For example, the volitive interjection “shh” or “shush” is used to command someone to be quiet.

Examples: Volitive interjections in a sentenceShh! I can’t focus when you’re singing.

Psst. Pass me an eraser.

Ahem. Please pay attention.

Emotive interjections

An emotive interjection is used to express an emotion or to indicate a reaction to something. For example, the emotive interjection “ew” is used to express disgust.

Curse words, also called expletives, are commonly used (in informal contexts) as emotive interjections to express frustration or anger.

Examples: Emotive interjections in a sentenceEw. I’m not eating that.

Yay! I’m so excited to see you.

Yum! This apple pie is delicious.

Cognitive interjections

A cognitive interjection is used to express a thought or indicate a thought process. For example, the cognitive interjection “um” can express confusion or indicate that the speaker is thinking.

Examples: Cognitive interjections in a sentenceUm, can you explain it once more?

Wow! I wasn’t expecting that.

Eureka! I’ve solved the puzzle.

Greetings and parting words

Greetings and parting words/phrases are interjections used to acknowledge or welcome someone or to express good wishes at the end of a conversation.

Examples: Greetings and parting words/phrases in a sentenceHey!

Hello! It’s good to see you.

Bye!

See you soon! Drive safe.

Interjections and punctuation

How an interjection is punctuated depends on the context and the intensity of the emotion or thought being expressed.

Exclamation points are most commonly used along with interjections to emphasize the intensity of an emotion, thought, or demand.

When the emotion or thought being expressed is less extreme, an interjection can also be followed by a period. If an interjection is used to express uncertainty or to ask a question, it should be followed by a question mark.

Examples: Interjections and punctuationOh. I don’t know.

We’ve just won the lottery. Hurray!

Hmm?

When an interjection is used as part of a sentence, it should be set off from the rest of the sentence using commas.

Examples: Interjections within a sentenceHmm, how are we going to do this?

It was an interesting lecture, indeed.

The project is, uh, going well.

Other interesting language articles

If you want to know more about nouns, pronouns, verbs, and other parts of speech, make sure to check out some of our other language articles with explanations and examples.

Frequently asked questions Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

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Ryan, E. Retrieved July 19, 2023,

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Sources

Aarts, B. (2011). Oxford modern English grammar. Oxford University Press.

Butterfield, J. (Ed.). (2024). Fowler’s dictionary of modern English usage (4th ed.). Oxford University Press.

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Garner, B. A. (2024). Garner’s modern English usage (4th ed.). Oxford University Press.

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