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- [Voiceover] Science and human biology.

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What we need to work
on now is figuring out

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what really is human biology.

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And how does it integrate with science?

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Because human biology is not
just the human body itself.

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It's how we interact
with what's around us.

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That's where the science part comes in.

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Science can help us explain many things

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that are occurring.

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And society, well, we're
all part of society.

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So it looks at how society
integrates with science

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and groups the human body.

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So really, science itself
is a body of knowledge.

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Science itself is a process.

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Science itself is the study of the world.

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Now there is no one, single definition

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for what science really is.

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Some people will say,

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science is a collection of
all knowledge we've gained.

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Some people will say,

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science is a process of how you determine

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or figure things out.

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The books gives kind of
a general rationale here,

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science is the study of the natural world.

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So we're talking plants,
animals, rocks, dirt, air,

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everything around us in the natural world.

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But really, that natural
world is made up of knowledge.

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We know about trees.

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We know about animals.

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We know about rocks,
stones, and dirt and water.

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You name it.

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Us as human beings

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have gained a huge amount of knowledge

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about what's around us.

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But how do you gain that knowledge?

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You gain it through a process.

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The process is how you
gather or acquire knowledge.

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Now, the process of
gaining this knowledge,

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a lot of times, is the scientific method.

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You're probably thinking,

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oh, scientific method,
I've never used that.

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Well I'll be willing to
bet you probably have.

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You probably used it today.

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Because when you woke up

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and decided to get going for the day,

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whether you're going to
work, going to school,

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whether you're staying in, staying home,

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whatever your choice were,

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you decided on what clothes to wear

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based on what was going on.

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Would you typically walk
outside in the middle of winter

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wearing a T-shirt and shorts?

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No, that doesn't make any sense.

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Well, let's walk out in
the middle of summer.

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90-degree day.

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Put on the heaviest
winter jacket you have,

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put on gloves, put on mittens, whatever.

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Put on a hat, big boots,
bundle all up for 90 degrees.

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That doesn't make a lot of
sense either, typically.

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So, how do you know not to do those?

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Well, it's the scientific method.

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And we'll discuss how that plays a part

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in the next couple slides.

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So in the scientific method,

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there are a set number of steps.

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Does that mean it always goes
from point A to B to C to D?

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No.

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Typically, you first
observe and generalize.

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Well, you woke up and decided
what clothes to put on.

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Well, you look outside and there's snow.

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You probably aren't gonna
wear a T-shirt and shorts,

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leave the house.

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You've observed snow,

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you generalize that it's cold out.

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You formulate a hypothesis.

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Well, one of the things was,

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it could be an if/then statement.

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If this happens, then that must occur.

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So in the case of getting
dressed for winter,

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well, if I walk outside
in T-shirt and shorts,

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then I will be cold.

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Well, feel free to go ahead and test that.

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That's your testable prediction.

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The prediction is you'll
be cold outside in that.

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You experiment or observe.

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Well, how could you
experiment for this one?

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How could you experiment
to test that prediction,

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that hypothesis?

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That shorts and T-shirt
in winter will be cold?

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Walk outside, simple and easy.

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Walk outside.

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Do you feel cold, do you feel warm?

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That's all you have.

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Sometimes, though, you can't
just run the experiment.

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Sometimes you have to watch and observe.

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If you're making a hypothesis
or trying to make a prediction

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about animals in nature,

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or what animals will do
in a certain situation,

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you can't always set up an experiment.

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Sometimes you have to
simply watch what they do.

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And then probably one of
the most important steps:

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modify the hypothesis and repeat.

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In science, a lot of times,
your initial theories,

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your initial thoughts, your intial ideas

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will not be accurate.

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So you change them, you modify
them, and you try it again.

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All right, let's go through, step by step,

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get a little more specific
about what each one is.

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The observe and generalize.

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Well, the idea here is

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you make your generalizations
based on observations.

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So this is inductive reasoning.

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Now inductive reasoning,
think specific to general.

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You start with something very specific,

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and you pull a general idea

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from the specific details.

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Your observation could be,
every winter in the past

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was colder than the preceding summer.

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All right, that's typically pretty fair.

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So you think, okay,
every winter in the past,

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from a look at the data,
look at the temperatures,

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was colder than the preceding summer.

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So by looking at all
these specific numbers,

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look at the data,

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your can generalize, say,

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winter will always be colder than summer.

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Now do we know that to be 100% true?

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No, we can't.

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You can't predict what's
gonna happen in the future

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and be 100% right.

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What you're doing is, you're generalizing.

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You're saying, this has happened before,

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x number of years.

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Or x number of occurrences.

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So you think that will keep happening

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for x number of occurrences more.

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Every winter in the past

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was colder than the preceding summer.

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I'm willing to bet

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that's probably happened
for decades and decades,

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if not centuries and centuries.

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There's a good chance
it's gonna happen again

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for decades and decades more.

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Or centuries and centuries more.

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Hence the generalization,

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winter will always be colder than summer.

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A hypothesis is what comes next.

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You must formulate a hypothesis,

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which is a tentative statement

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about the natural world around you.

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Probably one of the key points here

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it is a tentative statement.

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When you're making a hypothesis,

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you think it's gonna be true.

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You think it may be accurate.

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But there's no way to know for sure

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whether it'll be accurate or not,

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until you continue on in your experiment.

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Until you continue on through
the scientific method.

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So once we have this hypothesis,

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this statement of what we think

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will be happening, or what
will occur, or what we'll see.

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You then make a testable prediction.

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Hypotheses should be tested

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under many different conditions.

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Why is that so important?

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Why is it so important

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to test under many different conditions?

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Why not just test it under one,

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whatever's happening outside now?

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Because there's always variables,

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there's always differences.

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Think about any drug company
that goes to clinical tests.

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Before any drug hits the
market, any FDA-approved drug,

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that's going through years
and years of clinical testing.

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With one of the last
phases being human testing.

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Now, these trials, do they
always pick, I don't know,

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age 20- to 25-year-old males?

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No.

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Do they always pick females, any age?

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No.

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What has to happen is,

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you want to have a diverse sampling.

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You want to have people of all all ages.

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You want to have people of both genders.

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If possible,

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you want to have as many
differences possible.

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Multiple races, multiple ethnicities,

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multiple age groups.

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Multiple healthy living styles.

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Multiple illnesses, if possible, I mean,

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you try everything you can think of.

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Because you want to see if any of these

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will have a negative
or problematic result.

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So these testable predictions

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should be based on what
you say of the hypothesis.

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Now we should be employing
deductive reasoning.

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So a lot of times what you'll do is,

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you'll think of it as an
"if" and a "then" statement.

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If this occurs,

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then that must occur.

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The key, though, is,

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it has to be very specific.

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You can't say,

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oh, if you take this
drug, you'll be better.

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Well, that's kind of general.

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Doesn't really tell you anything.

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But if all of a sudden you say,

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if you take ibuprofen,

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your muscle pain will lessen.

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Well, it's better, but

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how do you quantify,

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how do you really test "lessen"?

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So it's one of these things,

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you want to be as specific as possible.

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Make it as accurate.

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So maybe if you had someone that was ill,

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and they're taking ibuprofen.

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Or taking Tylenol, pick a drug, any drug.

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And you made the statement,

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if you take this drug,

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then the fever will be
reduced by two degrees,

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Fahrenheit.

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Well, that's pretty precise.

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You can easily test it.

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If they take the drug,

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did the temperature reduce
by two degrees Fahrenheit?

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But even make it more specific.

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You might say,

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if you take this drug,

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whichever drug you want to take here,

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then the fever will be reduced

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by two degrees Fahrenheit
in 30 minutes' time-frame.

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That's even more specific now.

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We know that, at the 30-minute point,

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you test the temperature of the fever,

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to see if that drug, ibuprofen, Tylenol,

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whatever was taken, whichever
specific drug you're testing,

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worked.

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By making it as precise,
as specific as possible,

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it makes for a better testing method.

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Because that way

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the test is also repeatable.

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So if it's a successful test, it works,

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you can repeat it, and
see if it works again.

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So you want to make things
specific and accurate,

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as much as possible.