Methodology of Scientific Research

also known as MSR

also known as MıSıR

also known as 🌽

Who

Why

How

What

- Assistant Professor at the Molecular Biology and Genomics Department, Istanbul University
- Mathematical Engineer, U. of Chile
- PhD Informatics, U Rennes 1, France
- PhD Mathematical Modeling, U. of Chile
- not a Biologist
- Applied Mathematician who can speak “biologist language”

Answer now with your voice

In this course we will speak about

Good practices doing science

We will learn to observe, communicate and collaborate better

We want to help you to become

- Better molecular biologists
- Better scientists
- Better professionals
- Better citizens

Lower failure rate in your experiments

Do better experiments

Ask better questions

Achieve larger impact

Ask better questions

Propose better answers

Create Knowledge, not only Data

Perhaps you will change your career later in life

Whatever you do, do your best work

Always follow

*good practices*

Perhaps you will win the lottery

(or inherit many million dollars from a distant uncle)

and you do not work anymore

Still, you need to understand the world

Enough so big companies cannot fool you

We need to *see the Big Picture*,

How did we get to here and now?

- Asking good questions
- Observing nature
- Estimating
- Measuring
- Evaluating uncertainty

- Finding reasonable theories
- Testing theories with experiments

- Estimation and Guesstimation
- Order of magnitude
- Confidence Intervals
- Error propagation
- Uncertainty budget
- Dimensional Analysis

(second part of the course)

One of the essential good-practices of laboratory work is **the
Lab Notebook**

They are essential if you want to get a patent for something you create

They are essential to carry long term experiments

We encourage you to use a good quality notebook every day

Do not trust your memory

Our homepage is at https://www.dry-lab.org/blog/2024/msr/

It is part of my blog https://www.dry-lab.org/

Homework and class slides are published there

I also have a YouTube channel https://www.youtube.com/@dr.andresaravena

Scientist work is to understand *Nature*

We start by *Observing* Nature, usually measuring values

These are **exploratory** experiments

This is the **first part** of our course

The thing we study must be reproducible, and we need to see that repetition

We can find them using plots, linear models, clustering, etc.

This is the most important part

Good answers to bad questions are useless

Good questions are good, even if we don’t have answers

We answer these questions using *models* and
*explanations*

Valid models should make *predictions* that we can test in the
lab…

These are **validation** experiments.

If the results do not match the prediction, we know that the explanation is wrong. Two steps back.

Now we publish our data and model, so other scientists validate or reject it.

The final validation is to be published.

If the paper is accepted and published, our work becomes part of our
shared human *knowledge*.

The goal of Science is to produce new *Knowledge*.

When we observe *Nature* we use our previous
*Knowledge*

We look for *new Patterns* that raise *new
Questions*.

“Noise becomes Signal”

(some people say)

Daniel Kahneman, 2002 Nobel Memorial Prize in Economic Sciences

**Fast**

- instinctive
- emotional
- automatic
*cheap*

**Slow**

- deliberate
- rational
- intentional
*expensive*

- Survival
- Save energy

So *System 1* is the default mode

Most of the time we use the *cheap* intuitive system

*Rational thinking (i.e. math) is not spontaneous*

One option is to be like Spock, and suppress all intuition

The other option is to *train our intuition*

This is why we have been practicing *estimating*
magnitudes

Not let our intuition fool us

Get a gut feeling about numbers

This last part is important, because we make decisions based on our feelings, and often we do not know what to feel about a number

At first, we estimate by powers of 10

(after choosing the appropriate *units*)

We even have names for some of them them:

deci, centi, milli, micro, nano, pico

deca, hecto, kilo, mega, giga, tera, peta, exa

In powers of ten, how many people live in Turkey?

Be brave, take a guess

(no books, no Google, no ChatGPT, no Internet, only guess)

When estimating a value, we usually can guess that the real value is somewhere between two values

In other words, we guess lower and upper bounds \(L\) and \(U\)

Choose the smallest value that seems right,

then the largest one

Remember: we are looking for two numbers

The *order of magnitude* of a value is its power of 10

More precisely, is the integer part of the logarithm base 10

We say that two quantities are *in the same order of
magnitude* if their ratio is between 0.1 and 10

i.e. if each one is less than ten times the other

Which of these populations are of the same order of magnitude?

- Istanbul
- Ankara
- Tokio
- USA
- UK
- China
- India

Instead of going \[10^{-1}, 10^{0}, 10^{1}, 10^{2}, 10^{3}\] we can increment the exponent by 0.5 \[10^{-1}, 10^{-0.5}, 10^{0}, 10^{0.5}, 10^{1}\]

Since \(10^{0.5} = \sqrt{10}≈ 3.16≈3\) we can say \[0.1, 0.3, 1, 3, 10, 30, 100,…\]

Our campus has a park, near Astronomy department

How many trees are there?

The speed of light is about 300000000 m/s

It is easy to miscount the number of “0”

Instead, we write 3×10^{8} m/s$

Better, in computers we write `3E8`

(This is called *exponential notation*)

Here `3`

is called *mantissa* and `8`

is
the *exponent*

In USA, a billion is a thousand millions

In the rest of the world, a billion is a million millions

(often called “a milliard”)

There are two conventions: short scale and long scale

To avoid confusion, better use *giga* or *tera*

or use scientific notation: 10^{9}, 10^{12}

Please read The Library of Babel by J.L.Borges