- [Voiceover] Science and human biology.
What we need to work on now is figuring out
what really is human biology.
And how does it integrate with science?
Because human biology is not just the human body itself.
It's how we interact with what's around us.
That's where the science part comes in.
Science can help us explain many things
that are occurring.
And society, well, we're all part of society.
So it looks at how society integrates with science
and groups the human body.
So really, science itself is a body of knowledge.
Science itself is a process.
Science itself is the study of the world.
Now there is no one, single definition
for what science really is.
Some people will say,
science is a collection of all knowledge we've gained.
Some people will say,
science is a process of how you determine
or figure things out.
The books gives kind of a general rationale here,
science is the study of the natural world.
So we're talking plants, animals, rocks, dirt, air,
everything around us in the natural world.
But really, that natural world is made up of knowledge.
We know about trees.
We know about animals.
We know about rocks, stones, and dirt and water.
You name it.
Us as human beings
have gained a huge amount of knowledge
about what's around us.
But how do you gain that knowledge?
You gain it through a process.
The process is how you gather or acquire knowledge.
Now, the process of gaining this knowledge,
a lot of times, is the scientific method.
You're probably thinking,
oh, scientific method, I've never used that.
Well I'll be willing to bet you probably have.
You probably used it today.
Because when you woke up
and decided to get going for the day,
whether you're going to work, going to school,
whether you're staying in, staying home,
whatever your choice were,
you decided on what clothes to wear
based on what was going on.
Would you typically walk outside in the middle of winter
wearing a T-shirt and shorts?
No, that doesn't make any sense.
Well, let's walk out in the middle of summer.
90-degree day.
Put on the heaviest winter jacket you have,
put on gloves, put on mittens, whatever.
Put on a hat, big boots, bundle all up for 90 degrees.
That doesn't make a lot of sense either, typically.
So, how do you know not to do those?
Well, it's the scientific method.
And we'll discuss how that plays a part
in the next couple slides.
So in the scientific method,
there are a set number of steps.
Does that mean it always goes from point A to B to C to D?
No.
Typically, you first observe and generalize.
Well, you woke up and decided what clothes to put on.
Well, you look outside and there's snow.
You probably aren't gonna wear a T-shirt and shorts,
leave the house.
You've observed snow,
you generalize that it's cold out.
You formulate a hypothesis.
Well, one of the things was,
it could be an if/then statement.
If this happens, then that must occur.
So in the case of getting dressed for winter,
well, if I walk outside in T-shirt and shorts,
then I will be cold.
Well, feel free to go ahead and test that.
That's your testable prediction.
The prediction is you'll be cold outside in that.
You experiment or observe.
Well, how could you experiment for this one?
How could you experiment to test that prediction,
that hypothesis?
That shorts and T-shirt in winter will be cold?
Walk outside, simple and easy.
Walk outside.
Do you feel cold, do you feel warm?
That's all you have.
Sometimes, though, you can't just run the experiment.
Sometimes you have to watch and observe.
If you're making a hypothesis or trying to make a prediction
about animals in nature,
or what animals will do in a certain situation,
you can't always set up an experiment.
Sometimes you have to simply watch what they do.
And then probably one of the most important steps:
modify the hypothesis and repeat.
In science, a lot of times, your initial theories,
your initial thoughts, your intial ideas
will not be accurate.
So you change them, you modify them, and you try it again.
All right, let's go through, step by step,
get a little more specific about what each one is.
The observe and generalize.
Well, the idea here is
you make your generalizations based on observations.
So this is inductive reasoning.
Now inductive reasoning, think specific to general.
You start with something very specific,
and you pull a general idea
from the specific details.
Your observation could be, every winter in the past
was colder than the preceding summer.
All right, that's typically pretty fair.
So you think, okay, every winter in the past,
from a look at the data, look at the temperatures,
was colder than the preceding summer.
So by looking at all these specific numbers,
look at the data,
your can generalize, say,
winter will always be colder than summer.
Now do we know that to be 100% true?
No, we can't.
You can't predict what's gonna happen in the future
and be 100% right.
What you're doing is, you're generalizing.
You're saying, this has happened before,
x number of years.
Or x number of occurrences.
So you think that will keep happening
for x number of occurrences more.
Every winter in the past
was colder than the preceding summer.
I'm willing to bet
that's probably happened for decades and decades,
if not centuries and centuries.
There's a good chance it's gonna happen again
for decades and decades more.
Or centuries and centuries more.
Hence the generalization,
winter will always be colder than summer.
A hypothesis is what comes next.
You must formulate a hypothesis,
which is a tentative statement
about the natural world around you.
Probably one of the key points here
it is a tentative statement.
When you're making a hypothesis,
you think it's gonna be true.
You think it may be accurate.
But there's no way to know for sure
whether it'll be accurate or not,
until you continue on in your experiment.
Until you continue on through the scientific method.
So once we have this hypothesis,
this statement of what we think
will be happening, or what will occur, or what we'll see.
You then make a testable prediction.
Hypotheses should be tested
under many different conditions.
Why is that so important?
Why is it so important
to test under many different conditions?
Why not just test it under one,
whatever's happening outside now?
Because there's always variables,
there's always differences.
Think about any drug company that goes to clinical tests.
Before any drug hits the market, any FDA-approved drug,
that's going through years and years of clinical testing.
With one of the last phases being human testing.
Now, these trials, do they always pick, I don't know,
age 20- to 25-year-old males?
No.
Do they always pick females, any age?
No.
What has to happen is,
you want to have a diverse sampling.
You want to have people of all all ages.
You want to have people of both genders.
If possible,
you want to have as many differences possible.
Multiple races, multiple ethnicities,
multiple age groups.
Multiple healthy living styles.
Multiple illnesses, if possible, I mean,
you try everything you can think of.
Because you want to see if any of these
will have a negative or problematic result.
So these testable predictions
should be based on what you say of the hypothesis.
Now we should be employing deductive reasoning.
So a lot of times what you'll do is,
you'll think of it as an "if" and a "then" statement.
If this occurs,
then that must occur.
The key, though, is,
it has to be very specific.
You can't say,
oh, if you take this drug, you'll be better.
Well, that's kind of general.
Doesn't really tell you anything.
But if all of a sudden you say,
if you take ibuprofen,
your muscle pain will lessen.
Well, it's better, but
how do you quantify,
how do you really test "lessen"?
So it's one of these things,
you want to be as specific as possible.
Make it as accurate.
So maybe if you had someone that was ill,
and they're taking ibuprofen.
Or taking Tylenol, pick a drug, any drug.
And you made the statement,
if you take this drug,
then the fever will be reduced by two degrees,
Fahrenheit.
Well, that's pretty precise.
You can easily test it.
If they take the drug,
did the temperature reduce by two degrees Fahrenheit?
But even make it more specific.
You might say,
if you take this drug,
whichever drug you want to take here,
then the fever will be reduced
by two degrees Fahrenheit in 30 minutes' time-frame.
That's even more specific now.
We know that, at the 30-minute point,
you test the temperature of the fever,
to see if that drug, ibuprofen, Tylenol,
whatever was taken, whichever specific drug you're testing,
worked.
By making it as precise, as specific as possible,
it makes for a better testing method.
Because that way
the test is also repeatable.
So if it's a successful test, it works,
you can repeat it, and see if it works again.
So you want to make things specific and accurate,
as much as possible.