I get a two-tail P-value of 0.09, if my alpha value was 0.055 and

I were to write a report for publication.

These results I would say, well,

I did not find a statistically significant difference.

But if I reported this P-value, it would be 0.04.

It is less than 0.05.

There is a statistically significant difference between these two groups.

Purely based on the way I chose the hypothesis.

And that is of course, absolutely wrong.

And that is why I emphasize so much that the statement of the hypothesis,

whether there is a difference, there's one alternate hypothesis.

So it can be either more or less, the mean of one group can be more or

less than the other or one tail that I already say beforehand that one group will

have a mean higher or lower than the other where I can go for one tail.

This must be set beforehand.

You cannot do the statistical analysis and then based on the results,

then choose which P-value you are going to take.

So you can see clearly here, what absolute difference it makes and

which alternate hypothesis you set for yourself.

So for this set of values, we have a P-value of 0.09.

But if we do a one tail test, 0.04.

This cannot be decided once analysis has been done.

For that reason, also I emphasize in this course that

we really want data to be made available freely.

If everyone can examine the data and understand why a certain hypothesis was

chosen, it is makes reading that journal article and understanding and

believing what is written so much better and I've got another little tab down here.

It's called fixed.

I just want to show you something else, as well.

Now this time, these values are fixed.

I can't you and they change annually, but look at this.

I have a two-tail of P-value of 0.07 and

a one tail up window three, I might have decided beforehand.

Well, we are going to go for a two tailed alternate hypothesis.

In other words, we say, we don't know the difference between the two loops.

One might be more.

Group two might have a larger mean than group one.

We said, we don't know.

It might go either way and I look at through these results now and

I see, well, group 1 had a mean of 101.5, group two had a mean of 106.

So certainly, group one had a lower mean.

And I go through my data again and I notice there's this one case of 120.4,

which is quite a bit higher than the mean of 101.

And I might come up with some reasoning,

some logical argument in my head that there was something wrong with that case.

I'm not saying it's a statistical outline.

I haven't done interquarter ranges to prove that it is.

I just think that I'm so close with my 0.07 and I wonder what was wrong with

this patient, and I can go through the file, and by some logical argument.

I can decide, no, no.

This patient definitely has to be removed.

There was something wrong with that measurement.

Let's delete that patient.

We can still do it.

We still have enough patients in each group.

Look what happened.

I suddenly, from omitting that one value have a statistically significant

difference between the two groups.

See how easy it is to change the P-value and to get to a desired P-value, and

I call it desired, so that it is statistically significant.

Now, I'm not showing you how to cheat.

I'm not saying that anyone in the literature does cheat,

anyone who does research does cheat.

The point here is that I really believe that data should be made

openly available and we should all know what went into that data analysis.

And by doing this course, you now have an understanding of why this is so

important and what must be in place for

you to trust In the P-value that you do find in the literature.