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In the previous class we learned
why experiments are so important.

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If you're not sure why we
should be running experiments,

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take a moment and review the previous lesson.

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But in today's class we're going
to learn some new terminology

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that we're going to use throughout the course.

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There are some important words
we have to be clear about.

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Every experiment you run
will always have an outcome.

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An outcome is what will happen
as a result of the experiment.

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It is what we are interested in improving.

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That's the subject of this course:
experimentation for improvement,

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so we need to have something to
improve, called the "outcome".

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Many experiments will also
have one or more factors.

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Factors are the things you
change to influence the outcome.

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In this course we consider cases with two
or more factors, but it is totally possible

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to run experiments with just one factor,
where you are only changing one thing

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to see the effect on the outcome.

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Let's use an example of growing plants
to see this terminology at work now.

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The outcome of growing these plants
could be the height of the plant.

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An important aspect about the outcome is that
it is always measurable, at least in some way.

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In other words, once you have finished your
experiment, you must have some measurement.

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In the plant example a different outcome

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that you could have used might have
been the average length of the leaves,

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or it might have been the
number of flowers on the plant.

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Both of these would be numeric measurements.

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In most experiments, the outcome is
a number, a quantitative measurement.

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But, a qualitative measurement is also possible.

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For example, perhaps the outcome is the colour
of the flower: light red, red, or dark red.

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That's a qualitative measurement;
a description of what happened.

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When we combine an outcome and
the need to adjust the outcome,

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we get what we call an objective.

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In other words, outcome plus a desire to
adjust the outcome equals the objective.

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Here are some examples of objectives.

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Maximize the height of a plant,
minimize the amount of pollution.

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We most often want to maximize or minimize our
outcomes, but sometimes we want our outcome

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to remain the same even though
we are changing the factors.

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For example, let's say you
want to change your recipe

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for your favourite pastry to be gluten free.

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Now your objective is, you want the taste
to be the same as the regular recipe.

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Your outcome is taste and
your objective is the same.

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Note that you don't always need
an objective to run an experiment.

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Every experiment always has an outcome.

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Not every experiment has an objective, though.

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But we usually have one in mind.

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One more thing.

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Sometimes you'll hear the word response
used instead of the word "outcome".

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In the area of experiments, the word
response and outcome mean the same thing.

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Our next piece of terminology
is the word "factors".

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In the growing plants example there
could've been three factors that you changed.

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The amounts of water that
you gave the plant each day.

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The amounts of fertilizer that
you gave the plant each week.

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And using a soil type A or a soil
type B. All experiments must have

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at least one factor that is changed.

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However, as you'll learn in the next few classes
that you should always consider as many factors

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as possible, not just one or two.

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So, to summarize, every experiment
will always have two components:

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an outcome and one or more factors.

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Let's try this quick exercise.

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At your company, your manager asks you to
change the recipe for biodegradable plastic.

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You've been making this plastic
for two years now,

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but recently customers have been complaining
the plastic does not break down fast enough.

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The recipe to make the plastic uses a specific
chemical call polylactic acid, or PLA.

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Next, I'd like to discuss a
bit more about the factors.

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We can distinguish between
two main types of factors,

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numeric factors and categorical factors.

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Numeric factors are referred to as quantitative,

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since they can be measured
as a numeric quantity.

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Let's go back to the plants example.

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We may decide to use 15 millilitres
of water, or 30 millilitres of water.

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We could use six drops of fertilizer,
or we could add ten drops of fertilizer.

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In these examples, we can
numerically measure the factor.

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A key point about numeric factors is that we are
able to select and adjust their numeric level.

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We also have categorical factors.

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These are factors that can take
on a limited number of values

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and are usually not quantified numerically.

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For example, we could use soil type A or soil
type B. We could put a bag over the plants

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to create a greenhouse effect, or
we could leave the plants exposed.

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The factors in your experiment can
either be numeric or categorical.

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And most experiments will
use both types of factors.

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The distinction between numeric and
categorical is not too critical -

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there are many practice quizzes in this
module to help clarify this concept.

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One last comment before we wrap up
this class, is that in this course,

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and in the area of experiments, you
will hear two words for the same thing.

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Factor and variable.

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People might say this experiment
has three variables.

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Or they could say this experiment
has three factors.

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Those two statements are equivalent.

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Now in the next module we are not going to
waste any time, we are going to jump right in

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and analyze the results from an experiment,

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even though you actually
haven't done the work just yet.

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See you next time.