There are two errors that always rear their head once you are learning about hypothesis testing — false positives and false negatives, technically mentioned as type I error and sort II error respectively.

At first, i used to be not an enormous fan of the concepts, I couldn’t fathom how they might be in the least useful. Throughout the years, though, i started to possess a change of heart. The more I understood and encountered these errors the more they began to excite and interest me. Seeing their real-world applications and uses helped me go from an uninterested student to an enthusiastic teacher.

You know those teachers who frantically mention a topic that no-one understands or wants to understand? Yeah, that’s me now! And it’s great, so i would like to bring you to my level of pleasure with this text by showing you ways these two errors have practical implications in several and interesting real-life settings. Then hopefully, after reading it, you’ll be itching to inform your loved ones all about false positives and false negatives. Lucky them!

Exclaimer: this text isn’t here to show you ways to differentiate between the 2. If you’d wish to get an understanding of the way to do this , I’ve have made an explainer video on the topic here.

Making errors your friend.

Which error would you say is more serious?

A false positive (type I error) — once you reject a real null hypothesis — or a false negative (type II error) — once you accept a false null hypothesis?

I read in many places that the solution to the present question is: a false positive. I don’t believe this to be 100% true.

The proper scientific approach is to make a null hypothesis during a way that creates you are trying to reject it, giving me the positive result. So, let’s say i would like to ascertain if this particular article is performing better than the typical of the opposite articles I even have posted.

With this in mind, the null hypothesis i will be able to choose is:

“The number of times my article is read are going to be less or adequate to the amount of comparable articles I even have posted”

If I reject the null hypothesis, this suggests one among two things.

1. This text performed above average — Great! There’s my positive outcome.

2. I even have made a kind I error. I rejected a null hypothesis that was true. My test showed that I performed above average, but actually, I did not. I got a false positive.

Yes, here my false positive features a bad outcome, i might inevitably think my article is best than its and from then on write all my articles within the same style, ultimately hurting my blog traffic. this may little question affect my career and self-esteem during a negative way.

What about the false negative?

This would occur if, say this text was a masterpiece of blog writing but my test demonstrated that it’s not even mediocre. Of course, i will be able to not plan to write articles during this style anytime soon. However, i’m a driven person who learns from his ‘mistakes’ so instead try different techniques and potentially create even better writings.

This is not the simplest outcome, i’ll have missed a chance but that’s in no way as devastating because the false positive.

Now, this is often a case where the worst situation is that the false positive, however, an important fact is that I stated the null hypothesis during a specific way. Had I swapped the null and alternative hypotheses, the errors would are swapped, too.

Let me show you.

My new null hypothesis:

“The number of times my article is read are going to be quite the amount of comparable articles I even have posted”

In a false positive situation, i might reject a null hypothesis that’s true. So, the test would show that my masterpiece is really mediocre or worse. Remember this phrase? That was the false negative from the previous example.

What this shows is that the 2 errors are interchangeable. Therefore, it’s all about the planning of your study; you’ll change things to assist you avoid the larger problem.

Finding the positive in the… positive.

When applying for employment within the data science industry, an interview question that always crops up is:

“Can you provide samples of situations when a false positive features a better outcome than a false negative?” (and vice versa)

Of course, you’ll use the above example, however, some academics don’t particularly wish to hear the thought of swapping hypotheses. I just wanted to prove some extent that everything isn’t so black and white when it involves this idea .

Plus, I even have plenty more examples for you that you simply simply can lay on your potential employer and show them that you really know your stuff. You’ll win them over in no time!

These examples have hypotheses that can’t be switched thanks to science or law (see, not so black and white). They do, however, give us situations where having a false negative isn’t the perfect . Of course, we’re still being a touch rebellious, but doing so within science and law, so, who can stop us!

To baby or to not baby?

When you take a test for pregnancy, you’re asking: “Am I pregnant?”

In hypothesis testing, however, you’ve got your null hypothesis:

“I am not pregnant”

Rejecting the hypothesis gives you a ‘+’ Congratulations! you’re pregnant!

Accepting the hypothesis gives you a ‘–‘ Sorry, better luck next time!

Biology determines this one, so no switching I’m afraid. Although tests can malfunction, and false positives do occur; during this case, a false positive would be that tiny ‘+’ once you are, actually not pregnant. A false negative, of course, would be the ‘­–‘ when you’ve got a touch baby growing inside you.

This is an honest example because the higher situation is entirely hooked in to your situation!

Imagine Someone has been trying for a toddler for an extended time then by some miracle their bioassay comes back positive. They mentally prepare themselves for having a baby and after a brief period of ecstasy, in some manner, they determine that they’re , in fact, not pregnant!

This is a terrible outcome!

A false negative for somebody who really doesn’t need a child, isn’t ready for one and when assuring themselves with a negative result, proceeds to drink and smoke are often incredibly damaging for her, her family, and her baby.

Swap these women’s situations, however, and you’ve got outcomes that, while not ideal, are far better .

Trivia time!

Pregnancy tests have advanced to attenuate the probabilities of a false negative. This does improve the test as while it might be unlikely that you simply would attend a doctor to verify a negative result, it might be sensible to with a positive result. There are variety of medical reasons to urge a false positive, but false negatives appear only thanks to faulty execution of the test.

AIDS tests

Here may be a more clear-cut example.

Imagine a patient taking an HIV test.

The null hypothesis is:

“The patient doesn’t have the HIV virus.”

The ramifications of a false positive would initially be heartbreaking for the patient; to possess to affect the trauma of facing this news and telling your family and friends isn’t a situation you’d wish upon anyone, but after going for treatment, the doctors will determine that she doesn’t have the virus. Again, this is able to not be a very pleasant experience. But not having HIV is ultimately an honest thing.

On the opposite hand, a false negative would mean that the patient has HIV but the test shows a negative result. The implications of this are terrifying, the patient would be missing out on crucial treatments and runs a high risk of spreading the virus to others

Without much doubt, the false negative here is that the bigger problem. Both for the person and for society.

Trivia:

Many doctors call AIDS results ‘reactive’, instead of positive, due to false positives. Before a patient is definitively said to be HIV positive, there are a series of tests administered . it’s not all supported one blood sample.

Positive, until proven negative

In many countries, the law states that a suspect during a criminal case is: “Innocent until proven guilty”.

This comes from the Latin

‘Ei incumbit probatio, qui dicit, non qui negat; cum per rerum naturam factum negantis probatio nulla sit’.

Which translates to: “The proof lies upon him who affirms, not upon him who denies; since, by the character of things, he who denies a fact cannot produce any proof.”*

Therefore, the null hypothesis is:

“The suspect is innocent.”

So only enough , a false positive would end in an innocent party being found guilty, while a false negative would produce an innocent verdict for a guilty person.

If there’s a scarcity of evidence, Accepting the null hypothesis far more likely to occur than rejecting it. Therefore, if the law was that the suspect is “Guilty until proven innocent.” with the hypothesis being “The suspect is guilty.” accepting the null hypothesis when false would end in many innocent people being imprisoned.

So, protecting one innocent person at the danger of (possibly) letting five guilty people go free seems worthwhile for several people.

With the law the way it’s , the overall consensus is that the false positive would be the larger problem. the thought of putting an innocent author bars is unsettling, as proving they’re actually , innocent once convicted isn’t simple. While a false negative would end in a culprit going free, it could find yourself with a case being reopened or, if the person may be a serial offender, he are going to be convicted at a later date anyway.

Trivia:

Until recently Mexico was using the ‘guilty unless proven innocent’ system. As a result, judges wouldn’t even open most criminal cases, because they might fear to place too many innocent people in jail. Since 2008, Mexico’s criminal justice system has been transitioning to ‘innocent, unless proven guilty’.

Every breath you’re taking , I’ll be watching you.

Breathalyzer tests are a necessary nuisance. Nobody wants to be stopped for a breath alcohol test, on the other hand nobody wants to be killed by a drunk driver either. Swings and roundabouts.

The null hypothesis: “You are below the alcohol limit.”

Again, only enough , a false positive would show that you simply are over the limit once you haven’t even touched an alcoholic drink. A false negative would register you as sober once you are drunk, or a minimum of over the limit.

Both problems do occur thanks to varying factors which will influence breath alcohol samples. To counteract the issues of false positives (losing your license, receiving fines or jail time), the law states that one can provide a blood or urine sample to prove their innocence (if they’re , that is).

With this in mind, a false negative is clearly the larger problem. Allowing drunk drivers to continue driving while assuming they’re sober is clearly dangerous to them et al. around them. While losing a couple of hours of your day may be a small price to pay if it helps keep more people over the limit, off the road.

Trivia:

Common alcohol levels at which individuals are considered legally impaired for golf range from 0.00% to 0.08%. the foremost common benchmarks round the world are 0.00%, also referred to as the intolerance , and 0.05%. The limit is that the highest within the Cayman Islands , standing at 0.1%. This doesn’t imply a better tolerance to drunk driving, so before hitting the road after a bottle of Jack Daniels, confine mind that the local police really do enforce the laws with frequent checks.

One person’s junk is another’s treasure.

The final thing i would like to speak about is SPAM emails.

Many websites will tell you something along the lines of: “Please, check your SPAM folder. the e-mail that we just sent you’ll find yourself there.”

Email providers increasingly use data processing algorithms to filter SPAM from what’s wanted. this is often a subject that deserves a piece of writing of its own. However, we are talking about things when emails get misplaced.

I was flabbergasted when, some weeks ago, I sent an email to my sister and her email provider marked it as SPAM! How dare they! the sole explanation I could come up with was that I used my personal mailbox to email my sister’s company email address. So, the algorithm saw no evidence that my email would be desired by my sister (maybe it knows something I don’t…). Therefore, it accepted the null hypothesis:

“This email is SPAM.”

If the algorithm rejects the null hypothesis, the e-mail goes through. A false positive would be mean your inbox having the odd email from Nigerian princes looking to marry you, or long-lost relatives posing for your bank details, in order that they can send you the massive inheritance from your great grandmother’s cousin’s step daughter’s cat.

A false negative could alright be the larger problem. you’ll miss out on a call for participation for an interview or the vacation snaps from your sibling, simply because they’re lost within the copious amounts of SPAM — that you simply half-heartedly skim through before deleting.

This is right down to personal preference though, some people are so infuriated with a notification going off on their phone, only to ascertain a pointless email, that a few of misplaced personal emails are alittle price to pay.

Trivia:

Over 95% of the friend requests you forward Facebook are accepted, as you always reach bent people you recognize . this is often often not true for SPAM accounts and this is one among the ways Facebook detects them. However, recently bots adopted a technique where they pretend to be attractive females and specialise in male users as their victims. Because male users, on the average , accept these friend invitations, it takes for much longer to detect the bots.

These are few common samples of once you can have false positives and false negatives. As you’ll see, the error that’s preferable really depends on things itself, your personal preference or how the study has been designed (and that you simply can just change the hypothesis if need be). So, I hope you won’t follow the overall assumption that false positives end in bigger problems and are now better equipped to supply solid examples to back it up.