So I'm just gonna focus on three of the primary critiques leveraged at, at NHST.

And it's a global phenomenon.

Of course they keep merging and so on, but as you look at higher redshifts, galaxies become an increasingly more biased tracer of the underlying mass distribution.

And you can see that it was higher in the past.

Meaning that it wasn't biased by cultural background or ethnic background.

We can't get rid of it.

So just by getting a large sample you can get small effects to be significant.

The more of these books you read, you're learning about some of your biases, but you're making some of your other biases essentially worse.

If we could do that, would we want to take people's optimism bias away?

That is for layer two of the network and we also add those bias terms.

One problem, though, with the test retest method, is it won't reveal that bias.

This is a graph demonstrating the presence of publication bias in studies of publication bias.

Let's talk about a problem that often occurs in robotics called a systematic bias.

Put differently, the robot oscillates a bit like this with a fairly constant new offset error due to this bias.

In which case, we have to somehow clean this bias.

less a flat, constant straight line.

And a lot of research since then has shown that he was flat out wrong.

Max. texture LOD bias

Rendering intent determines the bias in the color conversion.

But before we get into that subject proper, we have to introduce another concept.

And there's another cognitive bias

It'll just everybody's temperature is increased and if it's consistent from time one to time two the correlation will be high but that won't reveal the bias.

Now we interfere with the region that we found to integrate negative information in this task, and the optimism bias grew even larger.

If the dark matter was associated with galaxies, it was in same general, we think.

What we observe is always light, not mass.

And that kind of correlational, analysis is what was done after inaudible introduced the test to really drive home the point that it, it truly was culturally biased.

These have to be identified by scientific investigation.

A lot of good experiments in this.

But individuals with severe depression, they have a pessimistic bias.

And that anticipation enhances their wellbeing.

One interesting thing about cognitive biases is they are the subject of so many books these days.

(Video) SP ... Prospect theory, hyperbolic discounting, status quo bias, base rate bias.

If they equal your feelings, you don't even know you have a model.

And this work corrects that bias in our culture, that dehumanizing bias.

But it does it for everybody uh, so it's just a little bias in the measurement uh, that's across the entire sample.

But as we go deeper in the past, higher ridges, then we see a.

I figured if it's 2 n it's going to be fine.

The idea behind classical test theory, and this, this comes from, classic work in psychometrics, the study of intelligence, in psychology, is that these raw scores aren't perfect.

A single strand dipped in water that completely self folds on its own into the letters M I T.

That produced fairly unbiased set of galaxies that enabled astronomers to go deep into the zone of avoidance.

If you think about how capitalism works, there is a bias here.

First of all, I'm going to show you the average amount of bias that we see.

So we have trouble with the risks that aren't very common.

The key here really is knowledge.

And so, if you see this combination, that's a sign that your algorithm may be suffering from high bias.