शैडोइंग अभ्यास: 99 - वीडियो के साथ अंग्रेजी बोलना सीखें

Every statistics course draws the same little square.

The truth is one of two things, and your decision is one of two things, so there are four boxes, two of which are mistakes,

and the two mistakes get names, type 1 and type 2, printed side by side as though they were two of a kind.

They are not, and the difference between them is the whole subject.

The box where nothing was going on and you called it real, the false alarm, carries a number you picked yourself before collecting a single observation,

and that number is 0.05.

The box below it, where something real was there, and you walked straight past it, has no number attached to it anywhere,

not on your screen, and not in the paper, and usually not in your head either.

So let's go and find the second number, because once you can see it, the sentence we have all written at some point,

no significant difference, stops meaning what you thought it meant.

Let's make this concrete with about the smallest real decision I can think of.

You change the color of a checkout button, you send some traffic to each version,

and you want to know whether the new one genuinely converts better or whether you are looking at noise wearing a costume.

The null hypothesis is the boring one, that the change did nothing, and it gets to be the starting point,

not out of fairness, but because it's the only hypothesis specific enough to make predictions.

If the button does nothing, the difference you measure is pure sampling wobble, and dividing that difference by its own standard error,

which is simply how much a number like this bounces around from one repeat of the experiment to the next, gives you a statistic whose distribution you already know.

It's a bell centered on zero, whose spread is exactly 1.

So from here on, one step along that axis means one standard error.

Now draw a line.

Pass the line you call the result real, short of it you don't.

And since you only care about the new button being better, one line on the right is all you need.

Put it where 5% of the bell is left stranded beyond it, which lands at 1.645 and shade that sliver in,

because area under a curve like this is probability, so the sliver is telling you how often you would cross the line in a world where the button did nothing.

That area is alpha, the type 1 error rate.

Alpha isn't measured, it's declared, and notice what that buys you, because you just computed it with no data, no knowledge of the true effect,

and without running the test at all.

So, try the same move on the other error.

You want the probability of missing a real effect,

which means the probability that your statistic lands short of the line in a world where the button does work,

and for that you need to know where the statistic sits in that world.

But the alternative hypothesis, as it is almost always written, only says the effect isn't And not zero is not a location.

No curve, so no area, so nothing to compute.

The only way forward is to stop being vague and name a specific effect out loud.

Say the lift is big enough that your statistic now averages three standard errors out instead of zero.

Then, there is a second bell, identical to the first, in every respect except where it's centered.

And now, the question has an answer.

Because beta is the piece of that second bell lying on the wrong side of your line,

the region where a real effect turns up looking ordinary enough to wave through.

With the line still at 1.645 and the alternative 3 standard errors out,

that shaded piece works out to about 9% of the second bell.

But 9% came with strings attached, because it was 9% for that effect.

Shrink the true lift and the second bell slides back toward the first.

The two overlap more and beta climbs.

So there is no such thing as the type 2 error rate of a test, only beta at an effect size somebody was willing to commit to,

which is exactly why alpha is printed on every table you have ever read and beta on almost none of them.

Alpha needs a null, and everybody has one of those lying around, while beta needs a guess about the truth, and nobody wants to write theirs down.

Now that both errors have pictures, watch what happens when you get nervous about false alarms, and pull the line further to the right.

The tail beyond it shrinks so alpha drops exactly as you wanted, but that line swept through the second bell as well,

and everything it passed over went from caught to missed, and the strip it swept is thin under the null and fat under the alternative,

since the alternative keeps most of its mass out in exactly that region, so beta climbs by a great deal more than alpha falls.

The numbers make the lopsidedness plain, with the alternative still 3 standard errors out.

At an alpha of 5%, you miss a real effect about 9% of the time.

Tighten alpha to 1%, which most people would call ordinary caution rather than paranoia, and you saved 4 points of false alarm,

and paid 16 points of miss, because the miss rate jumps to 25%.

Tighten to 1 in 1000, the kind of threshold people reach for when they want to sound rigorous,

and you now miss a real effect 54% of the time,

which makes a test that's strict worse than a coin flip at noticing the very thing it was built to notice.

And nothing about the world changed while we did that,

because nobody collected another observation and the button is exactly as good as it always was.

So the line is the only thing here you actually control.

And the cleanest way to see what you get for putting it somewhere is to flip beta over.

1 minus beta is the probability of catching a real effect when there is one, and that is the power of the test.

Because beta depended on which alternative you named, power inherits the same dependence.

So power isn't a number that belongs to your test the way alpha does.

It's a whole curve, with one value for every size of effect the world might be hiding from you.

So sweep the true effect from nothing up to enormous and plot what the test does at each point.

Choosing which point on that climb you need to be standing at is most of what designing a study actually is.

Far out on the right, the second bell has slid clean past the line, and power sits at essentially 100%,

because an effect that large is impossible to miss.

Walk back towards zero, and the curve sags, and the left-hand end is where it gets interesting.

Because when the true effect is exactly zero, the power does not drop to zero, it settles at 5%.

It settles at alpha.

That isn't a coincidence to memorize, is the definition unfolding, because when the alternative bell sits exactly on top of the null bell,

the chance of landing past the line is simply the tail area, and the tail area is what alpha was in the first place.

Which brings us to the sentence this whole video is really about.

Two teams run the same test on the same button at the same 5%, and the button really does help,

by a small amount, the sort of lift that is worth having, and that you would never spot by eye.

The first team routes a modest slice of traffic for a few days, and in their sample that true effect is worth one standard error.

The second team runs it across the whole site for a month, which is 16 times as much traffic,

and since the standard error falls like one over the root of the sample size, 16 times the data cuts that error to a quarter of what it was.

So the very same real effect is now worth 4 standard errors.

Both teams get a statistic that falls short of the line, and both write down the same sentence,

no significant difference, and since we stipulated that the button really works, both of them are wrong.

Only one of them was entitled to be.

For the first team, the two belts overlapped so heavily that their chance of catching that lift was 26%,

well under the 80% usually treated as the minimum worth running.

So being wrong was the likely outcome, expected about 3 times in 4,

and their sentence carries almost no information about the button at all.

For the second team, the chance of catching it was 99%.

So being wrong meant drawing a card that comes up about once in a hundred,

and their identical sentence is worth something precisely because it was so unlikely to get written.

Same words, same alpha, same underlying truth, and one of those sentences is nearly empty while the other is close to decisive.

A negative result never says there is no effect.

It says that if there is one, it was too small to be seen with the sample you had.

And how small that is, lives entirely in the number neither report printed.

Everything so far has been a trade, so it's fair to ask whether you can ever have both.

You can, but start with what doesn't work, because collecting more data does not lower alpha.

Alpha is wherever you put a line, so leave the line alone and quadruple your sample,

100

And your false alarm rate is still exactly 5% since the null bell is standard by construction

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and doesn't care how much data went into building it.

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What the extra data moves is the other bell.

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We just watched the root and rule work against the first team and pointed the other way it works for you.

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Because four times, the data doubles the distance between the two curves.

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And distance is exactly what you were short of.

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Take that alternative sitting 3 standard errors out, where the line at 5% left you missing 9% of real effects,

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then quadruple the sample so the separation becomes 6, and put the line at 3, which is exactly halfway between the two bells.

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Halfway makes the two tails mirror images of each other, so alpha and beta come out identical.

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both of them about 1 in 750.

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Neither error was traded away for the other.

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Both of them came down by more than a factor of 30, and the invoice for that came to 4 times the data.

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The only other way to widen the gap is to make the effect itself bigger.

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And that is usually a property of the world, rather than a knob on your desk.

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So the line is a trade and more data is an escape and both of those quietly assume you ran one test.

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Run several and alpha starts leaking in a way the single picture hides.

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Because alpha is a rate per test and rates accumulate.

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Run one test at 5% and you have a 1 in 20 chance of a false alarm, which most people accept without much thought.

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Run 20 independent tests over the same data set, 20 button variants, or 20 outcomes you happen to record, and the chance

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that at least one of them lights up in a world where nothing at all is happening is not 5%, it's 64%.

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A room containing nothing but noise hands, you a significant result more often than not,

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and every one of those results carries a p-value under 0.05, exactly as alpha promised.

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The standard repair is to divide the threshold by the number of tests you ran.

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So 20 tests each get an alpha of a quarter of a percent, and the chance of any false alarm anywhere comes back down to roughly 5%.

124

But look at what you just did to every one of those tests, because you moved each line further out.

125

And by now, we know exactly what that costs.

126

Against that same alternative, three standard errors out, power drops from 91% to 58%,

127

so a study that was properly powered for one comparison becomes underpowered for 20%, without anybody touching the data.

128

Every repair in this video has been somebody deciding which of the two errors to spend,

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which is why 0.05 deserves rather more suspicion than it normally gets.

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It isn't derived from anything, it's a convention that hardened into a habit, and adopting it without a thought silently announces

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that a false alarm is the mistake you would rather avoid in every situation for the rest of your career.

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Sometimes that is exactly right.

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If you are deciding whether a new drug reaches the market, a false alarm puts something useless on the shelf,

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and every study built on top of it inherits the mistake, so holding alpha very tight is defensible,

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provided you say out loud what you are buying it with, which is a higher chance that a compound that genuinely works dies in a trial too small to see it.

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And sometimes the posture is backwards.

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If you are watching for a rare structural failure, a false alarm costs you an inspection,

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and a wasted afternoon, while a miss costs you the failure itself.

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So the sensible design drives beta as low as it will go

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and simply pays for the drizzle of false alarms that comes with it.

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And sometimes the answer is to refuse the choice, which is what a two-stage design is for,

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where a cheap screen runs at a deliberately loose alpha so its beta is tiny, and almost nothing real slips past,

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and everything it flags goes on to an expensive test whose alpha is orders of magnitude tighter.

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Neither stage escapes the trade.

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What the pair of them does is spend the loose alpha, where testing is cheap, and the tight alpha,

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where it is expensive, so that the errors surviving to the end are rare on both sides.

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So those two boxes were never two of a kind.

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One is a promise you make before you look, the other is a consequence you inherit and can only pin down by naming what you were hoping to see,

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and the only question a test really puts to you is which of the two you would rather be wrong about.

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If you found this helpful, hit that like button, subscribe for more, and drop a comment if you have any questions.

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See you in the next one.

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Bye-bye!

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो में शैडोइंग के लिए 152 वाक्य और 2428 शब्द हैं। बोला गया हिस्सा 15:41 लंबा है। वक्ता लगभग 155 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। 85% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
alpha संज्ञा	/ˈæl.fə/	अल्फ़ा
alarm संज्ञा	/əˈlɑɹm/	चेतावनी
curve संज्ञा	/kɚv/	चक्कर, घुमाव
tail संज्ञा	/ˈteɪ̯l/	पूंछ, पूँछ
probability संज्ञा	/ˌpɹɑ.bəˈbɪl.ə.ti/	प्रायिकता, संभावना
climb क्रिया	/ˈklaɪ̯m/	चढ़ना
sweep क्रिया	/swiːp/	सफ़ाई करना
hypothesis संज्ञा	/haɪˈpɒθɪsɪs/	परिकल्पना
ordinary विशेषण	/ˈɔɹdɪnɛɹi/	साधारण, मामूली
collect क्रिया	/ˈkɑlɪkt/	शामिल होने, इकट्ठा करना
shade संज्ञा	/ʃeɪd/	छाया
observation संज्ञा	/ˌɑbzɚˈveɪʃn̩/	अवलोकन, प्रेक्षण
divide क्रिया	/dɪˈvaɪd/	बांटना
threshold संज्ञा	/ˈθɹɛʃ(h)oʊld/	डेवढ़ी
mirror संज्ञा	/ˈmiɹɚ/	दर्पण, शीशा

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्द	अर्थ
come back क्रिया	वापस आना

ध्यान देने योग्य उच्चारण

वक्ता 8 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे isn't, doesn't, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: hypothesis /haɪˈpɒθɪsɪs/, threshold /ˈθɹɛʃ(h)oʊld/, thin /ˈθɪn/, theirs /ðɛɚz/
“sh” और “zh” की ध्वनियाँ: shade /ʃeɪd/, observation /ˌɑbzɚˈveɪʃn̩/, threshold /ˈθɹɛʃ(h)oʊld/, essentially /ɪˈsɛnʃəli/, structural /ˈstɹʌk(t͡)ʃəɹəl/
लंबे शब्द — बलाघात सही रखें: statistical /stəˈtɪs.tɪ.kəl/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, hypothesis /haɪˈpɒθɪsɪs/, ordinary /ˈɔɹdɪnɛɹi/, genuinely /ˈd͡ʒɛn.ju.ɪn.li/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर alpha, alarm, curve जैसे शब्दों पर ध्यान दें।

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is left · isn't measured · is printed
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have ever read · has been
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	month, which is · with, which is · choice, which is

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: 99 - वीडियो के साथ अंग्रेजी बोलना सीखें

RE2 Unit 11B

Gaussian Naive Bayes, Clearly Explained!!!

The Professor Who Taught People How To Think (1962)

Introduction to statistical prediction models

This 30-Second Trick Solves Every Group Argument

Data vs. Findings vs. Insights

How to go from B1/B2 to C1? (in 5 minutes)

1. Introduction to statistics - Lecture 1