Hi. It's Mr. Andersen and this
is AP Biology science practice 5. It's on
analyzing data and evaluating evidence. Remember
in the last two practices we talked you know,
good questioning and then good collection
of data. But once you have a bunch of data
then you have to start looking through it
and telling, is this good data or bad data?
Is there extraneous data? Are there outliers?
How do I control for that? Then more importantly,
what does it tell me about my question? And
one person who collected a lot of data during
his lifetime what Charles David Keeling. And
he was collecting data on the amount of atmospheric
carbon dioxide at Mauna Loa Hawaii. And so
this is just a sampling of some of his data.
And when you look at it, the first thing you
might realize is, wow, this is overwhelming.
This is just data from one year. So I can't
tell anything from that. And so the first
thing you want to do is you want to organize
the data. And a graph is a great way to do
that. So what we're looking at is from 1960s
until today, this is the amount of atmospheric
carbon dioxide. And so we can see that it's
increasing and this is clearly tied to global
warming and the green house effect. You also
see annual cycling that we would have to account
for. That has to do with the sun moving to
the northern and southern hemisphere. And
so we get different amounts of plant growth.
And therefore we get varying amounts of carbon
dioxide. And so the first step is looking
at the data and seeing are there patterns
within this that I can learn from? But then
we also want to control for that. And so let's
say I give you the following question. How
does fertilizer amount effect plant growth?
And you collect a lot of data. Well looking
at that data I don't learn much until I start
to graph it and take a look at it. So if we
put fertilizer on the x and plant growth on
the y, now I see a relationship or a curve
of fit that says an increase in fertilizer
is going to give me an increase in plant growth.
What might happen after that? We could extrapolate
on what would happen if we increase it. But
now we could look at the y. Why is the increase
in fertilizer going to increase the amount
of plant growth. And so we can look at questions
like that. And so the college board is going
to ask you or test your ability to analyze
data in each of the following four big ideas.
And so if we're looking at evolution, they've
said that they could ask you questions related
to the history on our planet. Now they're
not going to ask you a lot of minutia questions
about learning all of the devonian, learning
all of the periods and eras and epics. But
they could ask you sequential questions or
gathering data from a certain era, what does
that tell us. In the area of free energy,
the sucrose lab is a great one they keep coming
back to. So this is again taking potatoes.
Putting them in different concentrations of
sucrose solution and then looking at what
happens to their percent change in mass. If
we're looking at information, this is clearly
signal transduction. And so how cells are
taking information outside and then responding
to that. So the whole blood glucose feedback
would be a great example of questions they
could ask you. And then the area of systems,
structure fits function. In other words, this
non competitive inhibition of an enzyme. And
so how does the structure of that competitor
molecule, how's it going to effect it's function?
And so let's look at some examples. And so
they're going to ask you questions in three
different areas. And the first one is they
want you to be able to analyze data to identify
patterns. And so this would be an example
of a short essay question they might ask.
In one paragraph explain biological factors
that determine the shape of the graphs pictured
above. And so this is clearly the perfect
example of the predator-prey relationship.
And they're asking you to look at biotic factors.
And so why is the prey going to vary like
this. And we could talk about you know the
food supplies, the amount of space that they
have. Maybe it's competition with other organisms.
Other prey species. And then interactions
with the predator. Why are we seeing an increase
in the predator species? Well we had an increase
in prey. So now predators are going to have
more young, but then as the prey drops off
the predators are going to drop off. And so
there's lots of areas that you could take
this into using the data that you're presented
here. Let's say we give you straight out data
set like this. So this is that potato lab
where you're going to put different potato
cores in different concentrations of sugar
water. So they have different molarity here.
This is the initial mass of the potato cores.
And then this is the final mass. So they might
ask you to identify possible sources of error
in the data set. And so we've learned so well
what happens if we have different concentrations
of sugar water, but maybe this data is wrong.
So if we look at it right here, I see that
there's no change in the 0.4, but the when
it's in 0.2 I'm seeing a decrease in that
mass. And that doesn't seem right. And so
maybe the beakers were mislabeled. And so
how could we revise the protocol to obtain
more valid data. And so be looking out for
that. Being able to take in observations and
then refine that. Where is the problem coming
from and then trying to correct that. And
sometimes the data is just going to be in
a multiple choice question. So right here
we've got a genetics question where we have
these tiny blue eyed Mary flowers. We've got
blue. But sometimes we'll have white and pink
it says in the description. So they're giving
you the crosses, the p generations, the f1
and then the f2. And as I look through this
I see this looks like a 3 to1, a 3 to1, and
this looks a little bit crazy down here. Almost
like a 2 to 1 to 1. And so which of the following
accounts for that explanation? So you may
want to pause the video and then take a stab
this question. As I went through it I was
able to rule out, I mean it sure looks like
inheritance. I was able to cross out the first
three and the right answer here is going to
be D. And so we're looking at is that there's
another gene product. And so this is epistasis.
We're having one gene effecting other genes
accounting for the different colors. And so
again data is amazing. We collect data. We
first have to visualize it and then we try
to explain it. And it's not always easy to
do that. Sometimes we have to look back at
our question. Was the question good? Was the
controls good? But once we have data, data
is amazing. And in one entity in the states
that collects a huge amount of data is NASA.
But they don't always know how to get that
data back to the people. And so this is a
group at NASA that's helping them to visualize
that. And they've created this animation called
the perpetual ocean which is looking at the
ocean currents. And it almost looks that a
Van Gogh. But if we run it we can learn a
ton from data. And I hope that was helpful.
