If the analysis was properly done, if the conclusions are fine,
then you write up the paper,
and then you send it to a journal where they will proceed to a peer-review.
Of course, in these days, we need to look up journals.
Because there are many journals that are also called "predator journals",
where they don't proceed to a peer review and it is just a big business.
So, for the non-scientific public it's often not quite clear if this is a predator journal.
HOW CAN WE KNOW THAT?
You can look up on the internet, there is a list that lists all predator journals.
COULD YOU TELL US MORE ABOUT PEER REVIEW?
Peer review basically just means that a paper is submitted to a journal,
Then the editor basically decides on the reviewers, which are usually two reviewers.
Then the reviewers read the article,
they look if the conclusions are sound.
They double-check the methodology.
And then they decide whether or not the manuscript is publishable.
Often, for high ranking journals, there are criterias,
like is it new enough or is it interesting enough?
And for really high-rank journals, they have their policies on what to publish or not.
Certain type of topics are often not interesting to them.
They usually go for what they think are the super interesting topics.
That's not necessarily always the case.
That's at least my opinion.
I think it depends very much on how many statistics are used.
During these days, we have a reproduction crisis.
That means, many of the articles don't reproduce.
Even when they seem to be properly done.
One of the reasons is that many people don't understand statistics really well.
That counts for the researchers but also for the public
who tries to read and understand these numbers.
Reproducibility means that one research group has found a result,
then other people try to reproduce this result
and, of course, you want them to find the same result.
Otherwise, there is a problem.
And that's a problem we had in the last years in quite many fields,
medicine is one, psychology is one.
Where people systematically try to reproduce statistics
in particular good ranking journals and they fail to do so.
That means many studies that claim that there's an effect on X,
for example, on nuts, on cognition, just didn't turn out to be true.
I think it's important to understand that we need more
than one number to understand what is going on.
For example, if there is a campain to improve education,
and before the campaign, maybe only one person was educated to read,
and if after, 3% of the population can read, that's a gain of 300.
But it still means that a majority of people cannot read.
So, we need to know two numbers in this case.
By the way, another good way to look at a statistic is not to ask
"is there an effect?" or "is there no effect?".
What you want to do, is to ask the question: "How big is the effect?"
For example, if nuts consumption basically prolongs your life,
then you might find it significant.
But what you want to do is to find out for how many days or years
the nuts consumption helps you.
That's a number you want to look at.
The second thing you want to do is to have a look at how many participants there were.
For example, if a study has just five participants, that's very bad.
If a study has thousands of participants, that's very good.
On the other hand, there is a trade of a study with thousand participants
can lead to significant results even if the effect size is very small
For example, an extension of two minutes of your life.
The first thing would be, be critical.
Don't believe everything.
The second thing is, just see how much sense it makes.
There are often claims that sound very weird, they are potentially very weird.
Just use your basic intuition to figure out if assumptions are true or not.
And if you think they are not true, maybe have a look.
As we just mentionned, look for the effects, how big they are.