Now when we ask a question in science, there're
a series of steps that we need to go through
so that it's systematic and logical, and we
call this the scientific method.
There are many different treatments of the
scientific method but these are the five basic
steps we're going to use: observation, hypothesis,
experiment, data, and analysis.
So let's go through each step.
Our first step in the scientific method is
very simply making an observation - hey, that's
a neat thing that I saw and I'm curious about that,
I wonder does this work or how does this work.
Observations generally lead to further questions
and to making predictions.
So let's say, for instance, that you're curious
about the effects of fertilizer on a plant;
so maybe what you want to do is get a nice
little house plant like this (that's a plant)
and put some fertilizer on it and see what happens.
Your observation is that fertilizers might
make plants grow faster.
The second step of the scientific method, and actually the most important step, is called the hypothesis.
A hypothesis is a tentative explanation for
why your observation is occurring; it's an
educated guess and this guess actually links
together two of the variables, two of the
things you observed.
There is a proper format for writing a hypothesis
and I would like you to use this, uh, format
in this class and essentially you say, 'Well, if A is true, then B must occur because of some reason.'
So if I see A happening or if I change A,
then B is going to happen and 'because' would
be why I think that happens.
A hypothesis is a claim that there is a relationship
between two variables, two things in the situation
and these variables are known as the independent
variable and the dependent variable; let me
give you an example to explain what these things are.
The independent variable is the variable being
tested; so let's say you're testing that fertilizer
thing on your plant, so the effect of fertilizer is what you want to test, that's your independent variable, OK.
Now, the dependent variable is the thing that
will change in response or, and/or, the thing
you're going to measure.
So let's say that in this experiment, you're
going to measure the height of the plant - that
is going to be your dependent variable.
Now, you can use these two variables to make
your hypothesis.
You can say, 'If I put fertilizer on the plant,
then it will grow faster' and you could add
a 'because something about nitrates or nutrients
and plants' if you wanted to add a 'because',
but a good hypothesis says, 'If I change the
independent variable, then the dependent variable
will change in a certain way because some
reason,' so there is a relationship between
these two variables.
After you've written your hypothesis, you
can perform an experiment; an experiment is
the actual testing that goes on and the design
of an experiment is really important.
Good experiments minimize variables, and what
do I mean by a variable?
Well, a variable is any factor that could
affect the outcome of your experiment, so
they're, you're testing your independent and
your dependent variables but they're always
going to be others involved, too.
For instance, with a plant experiment you
can have all of these other variables going
on: you can have predators such as slugs nibbling
on your plant, the wind, the amount of sunlight,
the temperature and the pressure, the amount
of water that you're giving it, the type of
fertilizer you're giving it, all of these things, even
the soil, can affect how that plant grows.
In order to minimize these variables, you
want to keep these factors as constant as
possible and if you can't control the variable,
then you really need to record that variable,
you need to keep measurements of it and see
how it changes, that way you can kind of,
um, account for it in the results of your experiment.
So good experiments try to minimize variables.
An excellent way to help minimize variables
is to run something known as a controlled
experiment; the controlled experiment involves
replications of your original experiment but
in these replications and these other experiments,
the independent variable has not been changed,
so they represent kind of the natural or normal
state before you started changing stuff.
Controlled experiments are important because
this control group gives you something to
compare your experiment to; it helps validate,
um, that relationship that you are claiming
exists between your independent and 
dependent variables.
Let me give you an example.
Here we have an example of a controlled experiment.
Let's say you're doing your little test with
the plants and the fertilizer; you have a
plant here, you've been putting fertilizer
on it, and you've been measuring the growth
of the plant over time.
Your plant is growing and that's awesome,
it looks really good, but you can't really
say that the growth is due to the fertilizer
if you don't have anything to compare it to
because you don't know how the plant was going
to react normally without the fertilizer.
So what you might do is include a control,
so here's our experimental group, right here,
here's our control group, so here we have
a plant that's the same type, maybe the same
size, same health levels, um, and you are treating it
with just water instead of water and fertilizer.
Now, the variables between these two plants
needs to be the same so the, the atmospheric
pressure, the amount of sunlight, the exposure
to predators, all of that stuff needs to be
the same between these two groups so you can
make an accurate comparison.
The only thing you, that you want to differ
between these two groups is the fact that
this one gets fertilizer and the fact that
this one doesn't; that way, if the fertilizer
plant grows faster than the water only plant,
you can say that it's the fertilizer causing
the increased growth.
Now this particular type of control is known
as a positive control; it's known to produce
certain results and positive controls usually
sort of represent the natural conditions before
you started changing things.
Another type of control that is sometimes
used is known as a negative control; in a
negative control situation, typically something
is removed so that you get the opposite results
to the positive control.
Negative controls are not always necessary,
it just depends on the experiment that you
want to do, but in this case if we wanted
to add a negative control, maybe we could
have a plant that we gave no water to - maybe
we suspect that the water is making it grow
more than the fertilizer so we'll give this
guy no water and see what happens, and there's
my prediction about what it will probably
look like; that's a negative control.
As you perform your experiment, you're going
to be collecting data and data can be presented
in a number of different ways: it can be quantifiable
or qualifiable, it can be presented in tables
or charts or pie graphs, it can be, um, mathematical
proofs, we have graphs, different kinds of
graphs, it can be photographic evidence, it
can be really nice drawings, or even really
good descriptions of what you see going on.
In this next step, you're going to analyze
your data and you're going to draw conclusions
about what you're seeing.
This is the point at which you are going to
relate your hypothesis back to your data and
you're gonna say, 'OK, does my data, um, support
my hypothesis or not, does it make logical
sense, was I able to repeat the experiment
more than one time and get the same results,
is my data reliable?'
This is the point at which you're going to
decide whether to accept or reject your hypothesis;
we really say 'accept' and 'reject' in science
rather than 'prove' and 'disprove' because
'prove' makes it sound like you're at the
end of the process and really you're not.
Now, remember that rejecting a hypothesis
is not a bad thing because science is a process
of elimination, but this is the point at which
you will make that determination; you might
decide that you need to go back and tweak
your hypothesis or tweak your experiment and
keep going, um, or you might decide that you've
arrived at the answer you were looking for.
Here's the data I collected from our little
plant experiment - kept track of the height
over a month and you can see the growth, when
we began without fertilizer and with, and
at the end and very simply, you can see that
the plant with fertilizer grew faster than
the plant without fertilizer.
So from this we might conclude that fertilizer
increases plant growth, we might accept our
hypothesis.
Again, we might want to go back and tweak
our experiment, and change a little, and try
it again - maybe more fertilizer makes it
grow even faster (you might want to test that,
OK), but that's the analysis and conclusion
part of the process.
Now in this class, usually you'll just run
an experiment once and that's sufficient for
our needs, but in real science at this point
in the method, we're gonna start over again;
again we're gonna tweak that hypothesis, tweak
the experiment, and try to replicate our results
and do these experiments over and over again.
If we were doing a plant fertilizer experiment,
we wouldn't just have two or three plants;
we'd want hundreds of plants so that we could
really collect a lot of data and make sure
that variables like genetic variation among
the plants wasn't affecting how they grow,
so this is just the beginning of the process
all over again.
Time for another Your Turn - you'll find this
Your Turn in the guided notes.
In this Your Turn, what I want you to do is
think about what might happen if you go out
in the parking lot and your car won't start;
you can actually use the scientific method
to try and figure out what's wrong with your
car and so what I want you to do is brainstorm
that and work through the steps.
Your observation is that your car won't start;
what possible hypotheses could you come up
with for why the car won't start, what experiments
would you do to test this, what data would
you collect, and what conclusion would you draw depending on what data you were able to collect, OK?
Write this out and bring it to class with
you.
