And, and this is one of the things we associate with different species, right?

Different species look different somehow. But there's another aspect of different

species too. Different species tend not to interbreed.

And so, if we now allow these drosophilia, these 2 populations to now

merge and hang around. And now, we watch their mating behavior.

What you will see is that they will only mate within species.

So the starch-eating ones will seek out other starch-eating ones.

And the maltose-eating ones will seek out other maltose-eating ones, and they will

not interbreed. This is the mark of them truly, being

individual species. So it certainly seems that by

manipulating through source. We were able to create two species of

drosophila, okay. So if you just sort of backing up a

little bit, because I want to give you some of this terminology too.

When we do an experiment like this, we're almost always manipulating some variable.

And then looking at the effects of that manipulation on other variables.

The one we manipulate we call the independent variable.

And in this case, that would be the food source.

So we, the experimenter, decided who was going to get which food source.

So we were in control of that, and we manipulated that.

OK? So that's what the independent variable

is. We thou, now want to see, okay, what

effects did that manipulation have? Well, on what?

So what are we going to measure to look for effects?

Well we want to measure species I, species I.

Speciation. Thank you.

We want to measure speciation and there's sort of two things relate this

speciation. One is species specific markings.

So we want to look at the physical characteristics for example.

That would be the yellowy color versus the darker color.

So according to that variable, the color of.

The drasphelia you do indeed see a difference and we are also curious about

this intermating because we know this is a marker of spec-, I've lost it again, my

goodness. of there being 2 different species

speciation there we go and so we want to look at mating behavior to see how often

they mate within species or at least within food source or across food source.

So those things would be what we call our dependent variables.

And we hope, or we expect, that if our theory's right, and we manipulate our

independent variable in a way where our theory predicts something should happen,

then when we measure our dependent variable, and contrast it across those

levels of the independent variable, which would be starch root versus maltose.

We expect to see differences okay, so we're really looking for differences now

of some depedent variable as a function of the independent variable.

when we do that, the test we use is actually a variety depending on the

design but the most simple test and the one we'll be playing with in the activity

in this course. Is something called a t test.

So I want to just give you a really general idea of what a t test is again

without getting in too much of the, of the guts.

But a t test is a ratio. Because what we really want to know,

let's imagine the color of the drosophila.

You can measure color on a continuum. And so, if this the starch-eating ones,

you would see some that were you know, up up, eh, it, it well frequency of light

let's say. And so let's say this is sort of a

yellowy color. Well some are more or less yellow, some

individual. So, most of them are probably right

around this yellow color. but there's a few that are, a little on

the paler side, and a few that are on the, the darker side, of the yellow.

here's this other group. Whoops, I'm sorry.

The maltose eating ones and we expect them generally to be darker but again, we

see a distribution where we have some really dark maltose ones and some not so

dark maltose ones. And we have this whole distribution.

And what we want to know is, on average. Did the starch eating ones seem different

than the maltos eating ones? Is the average starch-eating drosophila

more light than the average maltose-eating drosophila?

So, is this difference, something real, is it, is there a difference?

Well, that's a tricky question to ask, it turns out, and this is where we bring in

statistics, and a branch of statistics called inferential statistics.

Generally though, what it does is it says well, first of all I want to know how

different these two groups are. so we have our, you know, yellow ones and

our darker ones. Well how different are they?

And it When it's trying to decide if that difference is different enough it also

wants to know well, how different are individuals within each of these groups.

What's the variability within the groups? And so that'll give me some notion of how

much things vary just by chance when we don't manipulate anything.

And now we can look at the difference caused by the manipulation and this gives

us an idea of how big that is. Is, if this is a lot bigger than we would

expect by chance, then we're going to get excited and say we got something here.

But if it's not much bigger than we expect by chance, then we don't get that

excited. So we compute this t value.

And large t values. That is T values that is different from

zero. the further different from zero the more

excited we get about them. That means there's something going on.

And so literally, what we would do in an experiment like this is compute a T-value

based on the data we have, and then we would compare it to some critical value,

and this critical value would tell us What size of T is big enough for us to

get excited? And that'll depend on the, the number of

subjects we and, and a variety of other things that we're not going to get into

in detail here. But the general process would be to have

two groups, get some variable, like color where you measure the average for each

group, and you get a sense of a variability within each group.

You then compute a t value, and you compare that t value to the critical

value. And if your t is more extreme, I'm being

hedgey here because t's can be positive or negative depending on the order that

you subtract. And really the positive or negative isn't

important. It's how different from zero it is So if

a t is more extreme, bigger than the critical t, then it's real.

And we get excited, and we say hey, we found something.

Okay. You're going to get to do some of this.

This is partly why I'm taking the time here.

Because you'll be involved in this process.

And that'll give you a really good sense of it.

Alright?

>> So when you get a result like that, you say you have a significant result and

that's very exciting. Your experiment is a success.

But there's other, if we think in the bigger picture of what defines good

science, there are other factors. and you're going to play with all of

these factors in the digital lab code activity.

So one is, well it's really great that you found some difference.

But is that difference interesting? Is it really interesting, is it really

relevant. and this is really left up to the

scientists, to really make that case. And, and what I want to highlight here is

part of good science is good marketing. Those scientists who can express to

others why there data is important often get the headlines, and other scientists

who may have just as important findings but don't market it as well can sometimes

fall by the wayside. Nobody reads their papers.

So indeed marketing is part of science and marketing should be based on how

interesting or relevant Some result is. Another thing that's very important is

the notion of what we call replicability. And what this means is, okay, you went

out and you did your experiment say on some drosophila and you found a certain

result. Cool, what we'd really like is if

somebody else now redoes your experiment and finds the same results.

If that happens with a different set of flies.

And they do it all over again and they get the same thing.

Then we say they replicated your experiment.

And that makes us feel really good that we can trust your experiment, so

replicability is nice. What we then really hope to see is

generalizability. So what we mean by this is, okay, it

seems like the evolution theory, at least the prediction derived from it, worked on

drosphilia. Would it work on snakes?

Would it work on you know, various other kinds of animals we try?

So we might want to actually try it on spiders or on something else.

Do the same kind of experiment, generally, and if we see the same result

When we change aspects of the experiment, like the population, or like how we

measure things, then that makes us feel like this is a more general finding.

Something we can believe to be a general truth.

That yes, specia, specia, speciation, does seem to reflect changes in

environmental conditions. so these are other things and you'll be

able to play with some of these in that project.

Okay, so that's as much science as I'm going to nail you with, but I know right

now a lot of this stuff is ambiguous, but wait until you get your hands dirty.

Once you get your hands dirty, you'll start to see that this isn't only

understandable, it's kind of fun. and, and that's what Digital lab code is

all about. It's about making it fun.

Here just, I, I have a couple of videos that are really about experimental design

and the scientific method just to echo and in fact you know, go beyond some of

what I've told you. and now here are a couple of readings

about T-tests again, showing you a little bit of the math if, if you're interested,

and the general notion of comparing two different groups.