How to give a science talk in context of IYPT

Содержание

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Preview How it’s already done Who’s your audience What’s your message

Preview

How it’s already done
Who’s your audience
What’s your message
How to support the

message throughout
How to finish
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Problem statement Experimental setup Theoretical model Lots of experiments Theory and

Problem statement
Experimental setup
Theoretical model
Lots of experiments
Theory and experiment comparison
Conclusions
References

How you see

IYPT presentations

TheoreticalMathematical model

Because we start with a given problem
Because we built a really great machine
Because math rules and we know fancy function names
Because there are 4 parameters and we varied them all
Because our plots bend in the same direction
Because my teamlead told me so
Because they will complain if I don’t have this slide

Standard scientific

IYPT

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Think about your audience It cannot happen that most of your

Think about your audience

It cannot happen that most of your jury

board is simultaneously incompetent.
If they all don’t get what you say – it’s your problem.
It’s your job to do science work and make conclusions. It’s their job to listen.
When you’re not reporting, observe yourself observing a talk. What matters for you, what convinces you, what bores?
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Think about your message No elements of your talk are obligatory

Think about your message

No elements of your talk are obligatory and

Supreme Forces-required.
You want to say that you solved the required problem. Saying how much you struggled on it doesn’t help the case.
You prove that you’re correct by presenting a compelling argument.
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Crafting an argument Thesis Premises: Premise 1 Premise 2 Subpremise 1

Crafting an argument

Thesis
Premises:
Premise 1
Premise 2
Subpremise 1
Subpremise 2
Premise 3
Conclusion: thesis is true

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Crafting a physics argument Problem statement: Effect X is observed. Investigate

Crafting a physics argument

Problem statement: Effect X is observed. Investigate and

explain.
Thesis: Effect X is explained with theory T
Premises:
P1: Setup S is proposed and built
P2: Theory T is suggested
P3: Series of experiments E is conducted
P4: Results of E fit with predictions of T
Conclusion: Effect X is explained with theory T
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Nonlinearity of the argument P1: Setup S is proposed and built

Nonlinearity of the argument

P1: Setup S is proposed and built
P2: Theory

T is suggested
P2.1: Assumption A is used to build theory
P2.2: Theory T gives predictions
P3: Series of experiments E is conducted
P4: Results of E fit with predictions of T
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How to tell proud from truth Audience generally believes what you

How to tell proud from truth

Audience generally believes what you say.
If

you claim that you’ve done all the thinking work yourself, it is obnoxious.
Your novelty is only visible in contrast with existing knowledge.
Making unified conclusions is harder than measuring and writing formulas and reading papers. Be proud of your higher-level achievements.
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Why cite and reference Building up from basic physics is cool,

Why cite and reference

Building up from basic physics is cool, but

it’s unlikely that each your idea is original. Some ideas are, and conclusions are.
For this reason referencing contemporary research and journals is more respectable than referencing textbooks.
Often existence of reference is more important than its content.
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HOW TO MAKE THEM UNDERSTAND YOU Trick 1: Thin down/skip/gloss over

HOW TO MAKE THEM UNDERSTAND YOU

Trick 1: Thin down/skip/gloss over
Trick 2:

Walk-through
Trick 3: Dichotomies (comparing of two objects)
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Perception of many objects Human brain cannot process too many objects

Perception of many objects

Human brain cannot process too many objects at

the same time. It does not depend on the competence of the viewer, it depends on the quality of presentation.
Fortunately, you usually really don’t need to draw attention to many objects to convey your message.
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One bad plot – what do you see? A thousand points

One bad plot – what do you see?

A thousand points with

error bars

Some kind of trend, I guess…

Each single point of these is not important!

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One good plot – what do you see? I see one

One good plot – what do you see?

I see one line

Graphical

collapse of data

I see a number with uncertainty

Numerical collapse of data

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Trick 1 What did I just do with one plot? I

Trick 1

What did I just do with one plot?
I glossed over

all my raw data showing that I did it.
I distracted you by showing the trend line and the number, thinned down my data.
I skipped telling you the methods of these collapses, and you still believe me.
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A scary signal chain Start signal Stop signal

A scary signal chain

Start signal

Stop signal

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A plot analysis Background noise

A plot analysis

 

 

 

 

Background noise

 

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Trick 2 Why are these comprehensible? I showed you the whole

Trick 2

Why are these comprehensible?
I showed you the whole scheme/plot.
I put

animations showing technical details along the signal chain or plot analysis.
I walked you through the chain/analysis.
I used colored takeaway points.
I showed different information with the main scheme and the takeaways.
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Two plots shown together

Two plots shown together

 

 

 

 

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Picture vs plot Muons born Muons stop and decay Only muons

Picture vs plot

 

Muons born

Muons stop and decay

 

 

 

 

Only muons from this region
are

observed at ground level

 

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Trick 3 I broke the slide in two halves. Each half

Trick 3

I broke the slide in two halves.
Each half has a

comprehensible number of objects.
Two halves complement each other.
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On math Calculating magnet’s field Bio-Savart law Use Heaviside step fn

On math

Calculating magnet’s field
Bio-Savart law
Use Heaviside step fn
Fourier transform
Integral in Fourier

space
Use Bessel fns
Fourier transform back
Plot the field

Premise

Conclusion

Thin down/ skip

Proud in a physics talk

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On results and uncertainties Usually it is not possible to measure

On results and uncertainties

Usually it is not possible to measure anything

exactly.
Uncertainty defines the quality of result.
Larger data doesn’t just give larger proud, it gives smaller uncertainty in collapse.
Quoting uncertainties not just enhances your argument, but also can make it succeed or fail.
Discrepancy/uncertainty is a good gauge.
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Conclusions Conclusions are a reiteration of the argument, they are not

Conclusions

Conclusions are a reiteration of the argument, they are not surprising.
Your

goal was to coordinate theory and experiment results. Show that you did it.
Don’t stress the achievement of data. Stress the results and your confidence in them, that demonstrates data enough.
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How much of it? Optimal reporting speed is about 1-2 slides

How much of it?

Optimal reporting speed is about 1-2 slides per

minute.
If you want to show big data/scheme/math, don’t waste audience’s time in making them analyze it. It is your job. Use tricks to present.
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Acknowledgements/references References are best given along the presentation and reiterated in

Acknowledgements/references

References are best given along the presentation and reiterated in the

end. Remember, the audience cannot go back and forth as in reading a paper.
Thanking contributing people for helpful discussions is a nice touch.
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Common courtesies Provide the audience with the structure of your talk.

Common courtesies

Provide the audience with the structure of your talk. Show

section delimiters if it’s long, provide an outline, provide visible slide numbers.
Don’t invite anybody to your extra slides. Whenever you go there, you are almost surely lost. Same rules of proud vs truth apply to extra slides.
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If it’s so good, how to criticize it? Opponent’s performance is

If it’s so good, how to criticize it?

Opponent’s performance is making

an argument of critical evaluation, except for the conclusion is decided in the end.
You can challenge validity of the argument: is the conclusion true if premises are? Or you can challenge the soundness: are the premises true?
Your conclusion is that the reporter’s argument is either good and sound, or needs some work or fails.
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Attacking a physics argument Effect X Thesis: T X Premises: P1:

Attacking a physics argument

Effect X
Thesis: T<->X
Premises:
P1: Setup S
P2: Theory T
P3: Experiments

E
P4: E<->T
Conclusion: T<->X

Does it capture all the effects?

Are the assumptions true?

Are uncertainties evaluated correctly?

This is the Battleship game – you can’t sink the argument with one shot

Is there a numerical convergence with little uncertainty?

Are the theory and experiments sufficient to justify the conclusion?

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How to review it? You are to evaluate the argument of

How to review it?

You are to evaluate the argument of the

reporter and the counterargument of the opponent.
If they are in strict opposition, no more than one of them can win.
You can reconcile the two, ask for more investigation, or agree with one of them.