Okay, open your notebook. I've got the large
coffee black, no sugar, because we are in crisis
mode. And I've got the highlighters. We have
exactly one night to cram all of this into our
brains before Professor Pervitura's final exam.
Welcome to the psychology undergrad. Breathe.
Just breathe. We are going to be fine. It feels
like, you know, a mountain of information, but
it's really just a system. Think of this less
as a panic attack and more as a structured review
session. We've got the syllabus, we've got the
notes, and we have the exam must know terms sheet.
I'm trying to channel that calm. I really am.
But look at this sheet. It's huge. It's like
he took the entire dictionary, circled the most
boring words and said, memorize this or fail.
I mean, epistemology, operational definition,
homelessness. Wait, is that even on there? No,
thankfully that one isn't on the list. Look,
it's not about memorizing definitions. That's
the trap everyone falls into. If you just memorize,
you'll blank out when you see a scenario question.
We need to connect the dots. We need to understand
the story of research methods. The story? OK,
I like stories. Stories I can remember. So our
mission for this session is simple. We go through
the must know list. We define the terms. But
we also translate them into actual human speak.
No textbook jargon unless we Like, explain it
first. Exactly. And we're going to look at this
through the lens of everyday life. We'll use
analogies, pop culture, and maybe even touch
on how these things relate to bigger questions
of, you know, morality and belief. Because honestly,
that's where the stuff actually gets interesting.
Right. Because as dry as research method sounds,
it's basically the rule book for figuring out
why humans do the weird things we do. Yeah. It's
the user manual for the human experience. Precisely.
It is the rules of the game. If you want to play
the game of science, you have to know the rules.
Okay, let's start at the very beginning then.
Part one on the sheet. How we know what we know.
It starts with non -scientific ways of knowing.
So, this is the stuff humans did for thousands
of years before we had labs and white coats,
right? Right. These are the shortcuts our brains
use to make sense of the world. The first one
on the list is tenacity. Tenacity. That sounds
like a good quality, like grit. He's a tenacious
worker. In life, sure. But in this context, it's
actually a bit of a cognitive trap. Tenacity
is believing something simply because it has
always been believed. It's habit. It's superstition.
It's the idea that resists change just because,
you know, we've always done it this way. Oh,
I see. Like the idea that opposites attract.
Exactly. Or black cats are bad luck. Or, oh,
here's a good one, the idea that you have to
wait an hour after eating to swim, or you'll
get a cramp and drown. Wait, is the swimming
thing not true? I still wait 30 minutes. Just
to be safe. And that's tenacity in action. There
is basically zero data supporting the idea that
recreational swimming after lunch causes fatal
cramping. But we cling to it tenaciously because,
well, that's just what we believe. It's comfortable.
It requires zero mental effort to maintain. OK,
that makes sense. So tenacity is just habit.
Got it. Next on the list is intuition. The gut
feeling. This is when you believe something because
it just feels right. It's the hunch. Like when
you meet someone and immediately think, I don't
trust this guy. Right. Or the stock trader who
says they just feel the market is turning or
a parent who senses their kid is in trouble.
And here's the thing. Intuition isn't always
wrong. Sometimes it's actually like highly processed
pattern recognition happening below your conscious
radar. So what's the problem with it then from
a scientific standpoint? The problem is it's
purely subjective. I can't verify your gut feeling.
I can't peer review your hunch. If your evidence
is, I feel it in my bones, that's great for you.
But it's not data. It's just a feeling. Science
needs to be public and verifiable. You can't
replicate a gut feeling. OK, next one is authority.
This is a big one. Huge. This is accepting something
as fact because a respected source said so. Because
I said so. The classic parenting move. It's efficient,
right? We trust the surgeon general or the FDA
or professors. I don't own an electron microscope,
so I have to trust the physicist who tells me
what an atom looks like. We outsource the verification
to someone with credentials. Yeah, but the text
makes a specific point here about the difference
between authority in science and authority in
religion. I thought that was interesting. It
is a fascinating distinction. In a religious
context, authority is often the ultimate proof.
If it's written in the Bible or the Quran or
the Torah, that settles the argument for the
believer. The text is the authority and the text
is truth. Right. Faith often requires submission
to authority. Exactly. But in science, authority
is just a starting point, and often a shaky one.
The source warns us that authority can be wrong.
The FDA can make mistakes. Experts can be biased.
Science demands we question authority, whereas
faith often demands we submit to it. If Einstein
says something, we don't just accept it, we test
it. That's a really helpful distinction. Authority
is a shortcut, not a destination. Okay, moving
up the ladder of knowing. Rationalism. Now we're
using our brains. Rationalism is using logic
and reasoning to find truth. This is the Socrates
thing, right? The syllogism. All men are mortal.
Socrates is a man, therefore Socrates is mortal.
It's airtight logic. If the first two parts,
the premises, are true, the third part must be
true. It is, provided the premises are true.
But you can be logical and still be completely
wrong about the real world if your starting point
is off. Try this one. All birds can fly. Penguins
are birds, therefore penguins can fly. The logic
holds, but the conclusion is false because the
first premise, all birds can fly, is just wrong.
Exactly. Rationalism is garbage in, garbage out.
You can sit in an armchair and logically deduce
things all day, but unless you actually go outside
and look at a penguin, you won't know the truth.
And that brings us to the next one. Empiricism.
Empiricism. That's the, show me the money of
knowledge. I'll believe it when I see it. Pretty
much. It's knowledge through direct sensory experience.
I know fire is hot because I touched it. I know
the sky is blue because I see it. So science
is basically rationalism plus empiricism, logic
plus observation. In a way. But it's organized.
It's not just random looking and thinking. It's
the scientific method. And the text uses a great
analogy here. Science is a game. A game. Like
Monopoly. Think more like a sport. football,
or chess. A game is defined by its rules. If
you pick up the ball in the middle of a soccer
match and run into the goal using your hands,
you haven't scored a point. You've just cheated.
You're not playing soccer anymore. So the methodology
is the rule book. Exactly. Science has strict
rules. Randomization, control, peer review. If
you don't follow the rules, the points you score,
your results... They don't count. You can claim
you found a cure for something, but if you didn't
follow the method, nobody cares. Your claim is
invalid. I love that. That makes so much sense.
And the goal of this game, the sheet lists four
goals of science. Describe, explain, predict,
control. You need to know these in order. It's
like a progression. Let's break those down with
an example. Let's say... Procrastination. We're
definitely experts on that right now. Perfect.
First you have to describe. You have to define
what you're even looking at. What is procrastination?
Is it delaying a task? Is it doing something
else instead? You're just painting the picture
and defining your variables. Then you explain.
This is the why. Why do students procrastinate?
Is it fear of failure? Is it laziness? Is it
like a dopamine issue where you're seeking a
quick reward? You're looking for causes. Okay,
then predict. If we know the cause, we can anticipate
when it will happen again. If a student has high
fear of failure, they will likely procrastinate
on the final paper. We're making a testable prediction
now. And finally, control. This is the ultimate
power. If we can predict it, can we change it?
Can we manipulate conditions to make procrastination
go away? Can we design a workshop that lowers
fear of failure and thus lowers procrastination?
That's the end game. That's why we do this, to
intervene. Okay, so within this game, we have
Two main types of research on the list, basic
and applied. Think of it as the difference between
curiosity and problem solving. Basic research
is answering questions just for the sake of understanding
the mechanism. How does memory work? What is
the nature of love? How does the retina process
light? We aren't trying to fix anything yet.
We just want to know how the machine functions.
And applied research is the practical stuff.
Exactly. How do we treat Alzheimer's? How do
we reduce traffic accidents? It's taking that
basic knowledge and applying it to real -world
problems. They feed each other, you know? Basic
research on memory might lead to an applied treatment
for amnesia. And then there's the evil twin listed
here. Pseudoscience. The imposter. Pseudoscience
dresses up like science. It uses the jargon.
It wears the lab coat. It has charts and graphs.
Like those late -night commercials. Clinically
proven to melt belly fat in 48 hours. Usually.
The key giveaway, the thing to look for on the
exam, is that pseudoscience only looks for confirmation.
It ignores anything that proves it wrong. Real
science is obsessed with trying to prove itself
wrong. That's a huge difference. So pseudoscience
starts with the conclusion, this magnet heals
pain, and only looks for people who say, yes,
it works, while ignoring the thousand people
who say it didn't. Precisely. It's confirmation
bias dressed up in a lab coat. That's a good
segue into part two, generating ideas. Because
to do real science, you need an idea. And that
starts with the battle between hypothesis and
theory. I feel like people mix these up constantly.
All the time. In casual conversation, people
say, oh, I have a theory about why my roommate
is so cranky. In science, that's not a theory.
That's a hypothesis. So what's the difference,
technically? A hypothesis is a specific, testable
prediction. It's a bet you place on a single
outcome. If I give my roommate a cookie, their
mood will improve. That's a hypothesis. It's
narrow. It's specific. You can test it right
now. And a theory is bigger. A theory is the
big picture. It's a broad framework that explains
a whole body of evidence. It's like the picture
on the puzzle box after you've put hundreds of
little hypothesis pieces together. Give me an
example. Okay. The theory of evolution. It's
not a guess. It's a massive framework that explains
fossils, genetics, biology, and observation.
It's supported by thousands and thousands of
tested hypotheses. But the text makes an important
point here. A theory is never proven 100 % right.
Right. In science, we don't really use the word
proven. A theory is just the best explanation
we currently have. It can always be updated if
new evidence comes along. Newton had a theory
of gravity. It was great. Then Einstein came
along and said, actually, there's more to it,
and updated the theory with general relativity.
That's the humility of science. It's always a
work in progress. OK, that makes sense. Now,
to build these theories, we use logic. The list
has deduction and induction. Top down versus
bottom up. I always get these backwards. OK,
think of the shape. Deduction is like a triangle
pointing down, top down. You start with a general
theory at the top. And you deduce a specific
prediction down at the bottom. Right, right.
Like the front row example from the book. Exactly.
General theory. Students who sit in the front
row are smarter. So your deduction is, therefore,
if I look at that specific student, Dave, in
the front row, he will get an A. You go from
the general rule to the specific case. OK, so
general to specific. What about induction? Induction
is the opposite. It's bottom up, like a triangle
pointing up. You start with specific observations.
You start with the data. The dirt. So you'd see?
You'd see. Hey, Dave, in the front row got an
A. And look, Sarah in the front row got an A.
And Mike in the front row got an A. You gather
all these specific alterations. And from them,
you induce a general theory. Huh. Maybe all front
row students are smart. You're building the theory
from the ground up based on what you see. That
feels like Sherlock Holmes. It is. Although,
fun fact for the nerds listening, Sherlock Holmes
usually claims he's using deduction. But he's
actually using induction most of the time. He
sees a muddy boot, that's a specific observation,
and concludes the person came from the country,
a general conclusion, that's bottom up, that's
induction. Holmes was a liar. Okay, noted. Before
we can even form these hypotheses, we have to
do a literature review, which sounds so boring,
but the text calls it Standing on the Shoulders
of Giants. And it is. You have to know what's
already been done. You look at peer -reviewed
journals, psych -NFO, PubMed. You don't want
to waste six months running an experiment that
someone else already disproved ten years ago.
It's about being part of a larger conversation.
And there's a mnemonic here. The three C's of
a review. Be comprehensive. Be critical. And
be clever. Don't just read the abstract and call
it a day. Tear the study apart. Ask questions.
Why did they use that sample? Was there logic
sound? Is there a better way to test this? But
there's a danger lurking in those journals. Publication
bias. The file drawer problem. This is a really
serious issue in science. Journals want to publish
exciting, positive results. We found a cure or
we found a connection between X and Y. And nobody
wants to publish, we found nothing. Exactly.
If you run a perfectly good study and find nothing,
hey, eating kale has zero effect on math scores.
That study probably won't get published. It gets
stuck in the researcher's file drawer. So we
end up with a skewed view of reality because
we only see the hits and never the misses. Precisely.
It makes effects seem stronger or more common
than they really are because all the failures
are invisible. It's a huge problem. Okay, let's
get serious for a second. Part three, the moral
compass. Research ethics. Because science hasn't
always been the good guy. No, not at all. History
is full of the dark side of science. When we
prioritize knowledge over humanity, we get atrocities.
The text starts with the Nazi medical experiments.
It's horrific stuff, freezing people to see how
long they'd survive, infecting them with diseases
just to watch them die. This led directly to
the Nuremberg Code in 1947. The absolute number
one rule that came out of that, voluntary consent,
you cannot force people to be guinea pigs, period.
And it wasn't just the Nazis. We have the Tuskegee
syphilis study right here in the U .S. This one
is just heartbreaking. Researchers from the U
.S. Public Health Service studied the progression
of syphilis in hundreds of poor African -American
men for 40 years. 40! And they told the men they
were being treated. They told them they were
being treated for bad blood, which was a vague
local term. But they actually withheld the cure
penicillin even after it became the standard
treatment, just so they could watch the disease
destroy these men's bodies. That is, it's evil.
There's no other word for it. It is. It violated
every ethical standard we hold now. That study
is largely why we have the Belmont report. This
is guaranteed to be on the exam. The big three
principles. Yeah. We need to know these cold.
Okay. Number one, respect for persons. This means
autonomy. People must be capable of making their
own informed decisions. This is where informed
consent comes in. You have to tell them the risks,
the benefits, what they'll be doing, and that
they can quit at any time. And if you can't consent,
like children, or prisoners, or people with cognitive
impairments? They're considered vulnerable populations.
They need extra protection. For a child, you
need permission from a guardian, and you also
need assent from the child, meaning they agree
on their own level, even if they can't legally
consent. OK, number two. Beneficence. Do no harm.
It's the Hippocratic oath for researchers. Maximize
benefits, minimize risks. You always have to
do a risk -benefit analysis. Is the knowledge
we might gain worth the potential stress or harm
we might put the participant through? And three,
justice. Fairness. This goes right back to Tuskegee.
You can't just test on the poor or the vulnerable
to find cures that will only benefit the rich.
The burdens and benefits of research must be
distributed fairly across society. Okay, let's
unpack the specific terms on the sheet. Informant
consent we got. What about deception? Can you
lie to participants? Surprisingly, yes, but it's
a very delicate line. Sometimes, if people know
exactly what you're testing, they act differently.
If I tell you... I'm testing how honest you are.
You're suddenly going to be the most honest person
in the world, so I might have to deceive you
about the study's true purpose. But there are
rules for that, right? Strict rules. You can
only use deception if it's absolutely necessary
for the study to work, and you must follow it
with a debriefing. Debriefing. That's the coming
clean moment. Right. After the study is over,
you sit them down and say, OK, we're sorry we
told you the building was on fire. We were actually
testing your reaction time under stress. Here's
why we had to do it that way. Here's what we
hope to learn. And are you OK? You have to restore
their trust and make sure they leave in the same
state they came in. Then there's anonymity versus
confidentiality. These are not the same thing.
Crucial distinction for the exam. This trips
people up. Anonymity means even the researcher
doesn't know who the participant is. There are
no names, no identifying information linked to
the data. It's a double -blind data set, effectively.
Confidentiality. Confidentiality means I know
who you are. I have your name on a consent form,
but I promise to keep your data secret and secure.
Your name won't be in the final publication.
It's locked away. Got it. Now, we can't talk
ethics without talking about the bad boy studies,
the ones that probably couldn't be done today.
First up... Milgram. The obedience study. Stanley
Milgram was haunted by the Holocaust. He wanted
to know, how could ordinary people commit such
atrocities? So he set up a fake shock machine.
The participant, the teacher, thought they were
giving increasingly powerful electric shocks
to a learner in the other room every time they
got a question wrong. And the shocks kept getting
stronger, all the way up to a switch labeled,
danger, severe shock, and then just... X, X,
X. And the learner, who is an actor, would scream,
complain about his heart condition, and eventually
fall silent. But the experimenter in the white
lab coat would just calmly say, the experiment
requires that you continue. And the terrifying
results. 65 % of people went all the way to the
maximum voltage, ordinary people. It showed that
the power of authority can often override our
own personal morality. It's a profound finding,
but the stress it caused the participants. yelling
at the experiment or sweating, shaking. It was
highly unethical by modern standards. Absolutely.
Next, Zimbardo, the Stanford prison experiment.
Oh, man. They assigned psychologically healthy
college students to be either guards or prisoners
in a mock prison in the basement of the psychology
building. It was supposed to last two weeks.
It lasted six days. Because the guards went full
dictator. They became sadistic. They were verbally
abusive. They made prisoners do humiliating things.
The prisoners, in turn, had emotional breakdowns.
Zimbardo had to shut it down early. The study
showed how powerful social roles, the uniform
you wear, can create evil behavior. It wasn't
that they were bad apples. It was a bad barrel.
And finally, the last one on this list, Robber's
Cave. This one is actually a bit more hopeful
in the end. Muzaffar Sharif took two groups of
boys at a summer camp. He named them the Eagles
and the Rattlers. He first built group cohesion.
Then he put them in competition against each
other. And they grew to hate each other, like
true prejudice. They were raiding cabins, burning
flags. Lord of the fly style. Basically. But
then he investigated how to fix it. He found
that just bringing them together for a movie
night didn't work. It just gave them another
chance to fight. What worked was creating super
-ordinate goals. What's that? Shared goals where
they had to work together to succeed. The camp's
water supply broke and all the boys had to work
together to fix it. A truck got stuck and they
all had to pull it out. So shared struggle creates
unity. I like that. That's a powerful lesson.
Okay, my brain needs a break from the heavy stuff.
Part four, measuring the invisible. This is the
core challenge of psychology, right? In physics,
you can measure mass or velocity. In psych, we
are measuring feelings. We're trying to measure
things you can't see or touch. We are measuring
constructs. Love, anxiety, hunger, intelligence.
Yep. So the big question is, how do you measure
hunger? You can't put a ruler next to it. You
need a way to make it observable. You need an
operational definition. Yeah. This is the recipe
for measuring the construct. It's how you operationalize
it. Exactly. A bad conceptual definition of hunger
is feeling empty. That's subjective. I don't
know what your empty feels like. A good operational
definition is hours since last meal, or grams
of food consumed in the last 24 hours, or even
self -reported hunger on a scale of 1 to 10.
It's concrete and measurable. This matters a
lot. The prompt mentioned a moral link here.
Think about defining morality. That's a great
example. If I operationally define morality as
frequency of church attendance, I'm going to
get a very specific and probably very flawed
set of data. I'm going to completely miss the
atheists who volunteers at the soup kitchen every
single day. So how we choose to define our variables
fundamentally shapes the truth we find. Okay,
we have our definition. Now we need a scale to
measure it on. The acronym is N -O -I -R. N -O
-I -R. Nominal ordinal interval ratio. You have
to know these. Let's break them down. Nominal
is N for name. It's just categories. Junger,
eye color, political party, Ferrari versus Ford.
There is no order. blue isn't better or more
than brown. Got it. Ordinal is O for order. Right.
Rankings, first place, second place, third place,
small, medium, large. The crucial point here,
and this is key, is that the gap between the
ranks isn't necessarily equal. The difference
in speed between the gold medalist and the silver
medalist might be 0 .01 seconds. The difference
between silver and bronze might be 10 seconds.
We know the order, but not the distance between
them. OK, that makes sense. Then interval. Interval
scales have equal gaps. Think temperature in
Celsius or Fahrenheit. The difference between
10 degrees and 20 degrees is the same as the
difference between 20 and 30 degrees. It's 10
degrees. So the intervals are equal. But there's
no true zero. Right. Zero degrees doesn't mean
an absence of temperature. It's just another
point on the scale. You can even have negative
temperature. Which brings us to the last one
ratio ratio scales are like interval scales But
they have a true zero a meaningful zero point
height weight time number of questions answered
correctly So zero pounds means no weight zero
seconds means no time And because there is a
true zero, you can make ratios. You can say 100
pounds is twice as heavy as 50 pounds. You can't
say 100 degrees Fahrenheit is twice as hot as
50 degrees Fahrenheit. That math doesn't work.
That is the trickiest distinction, I think. Interval
versus ratio. The key question is, does zero
mean none of the thing? If yes, it's ratio. You
nailed it. OK. Next big concept pair, reliability
versus validity. The bathroom scale analogy.
This one is clutch. It's the classic for a reason.
Reliability is consistency. If I step on my bathroom
scale five times in a row and it says 150, 150,
150, 150, 150, that scale is highly reliable.
It gives me the same result every time. OK, so
what's validity? Validity is accuracy. Does the
scale reflect reality? If I actually weigh 150
pounds and the scale says 150, it is both reliable
A and D valid. But what if I actually weigh 150
pounds and the scale consistently says 170, 170,
170? Well, that's reliable, but it's not valid.
It's consistently wrong. Exactly. A broken clock
is perfectly reliable. It shows the exact same
time twice a day, but it's only valid for two
minutes out of the entire day. For science, you
need both. You need your measure to be consistent
and accurate. The text mentions different types.
test -retest reliability. Are scores stable over
time? If I give you an IQ test today and again
in a month, the scores should be pretty similar.
And inter -rater reliability. You two judges
see the same thing. Think of Olympic diving.
You want the judges' scores to be highly correlated.
If one gives a 9 .5 and another gives a 2 .0,
you have low inter -rater reliability. And for
validity, there's construct validity, which is
the big one. Are we actually measuring what we
think we are? Is our love scale really measuring
love, or is it just measuring, like, attachment?
And then there's face validity, which is just,
does it look like a good test on the surface?
Which is the least scientific type, right? Totally.
Buzzfeed quizzes have high face validity, which
Disney princess are you? Looks like it's measuring
that, but they have absolutely zero construct
validity. Okay, part five. We're getting there.
Sampling and research design. The who and the
how. Let's start with the who. Population versus
sample. Population is everyone you want your
results to apply to. The entire group you're
interested in. All college students in the United
States. All people with depression. Which is
usually impossible to study. So you take a sample.
The sample is the small subset of the population
that you actually study. The 20 kids in my intracite
class. The 100 people who responded to my flyer.
And ideally you want a representative sample.
One that actually looks like the population in
terms of, you know, age, gender, ethnicity, etc.
But usually what we get in psychology is what's
called a weird sample. This is a great acronym.
I remember this Western. Western, educated, industrialized,
rich and democratic. Most psychology research
is done on American undergrads who need course
credit. So the question is, does a finding from
a study of 19 year old Stanford students really
apply to a 60 year old farmer in Kenya? Maybe.
Maybe not. That's a sampling bias, and it's a
big limitation on the generalizability of our
findings. Okay, now for the how. The designs.
Non -experimental versus experimental. Non -experimental
includes naturalistic observation. Just watching
people in the wild. Yep. Like Jane Goodall with
the chimps. It has high external validity because
it's real life, but you have very low control.
You can't stop it from raining, and you can't
ask the chimps to repeat that cool thing they
just did. And it has some ethical issues, right?
Privacy. For sure. Then you have surveys and
correlational research. This is where you measure
two or more variables and see how they relate.
And this is where we get the golden rule of stats,
tattooed on every researcher's brain. Correlation
does not equal causation. Say it again for the
people in the back. Just because two things move
together doesn't mean one causes the other. The
classic example. Ice cream sales are positively
correlated with shark attacks. So does eating
ice cream cause you to be attacked by a shark?
No. There's a third variable causing both. Heat.
In the summer, it's hot, so more people go swimming
and more people eat ice cream. OK, so to prove
cause and effect, you need an experiment. The
gold standard. The gold standard. And you need
three pillars. The professor will definitely
ask about these. Number one is manipulation.
You have to actively change the independent variable.
Number two is control. You need a control group
that doesn't get the manipulation, and you have
to keep everything else constant. And number
three, the most important one is randomization.
You must use random assignment to put people
into your experimental and control groups. This
helps ensure the groups are equal to begin with.
And if you're missing one of those, especially
random assignment, then you have what's called
a quasi experiment. It looks like an experiment,
but it's not quite there. For example, comparing
men and women, you can't randomly assign someone
to be man or woman. So any differences you find
might be due to gender or they might be due to
something else you couldn't control for. Got
it. Even with a true experiment, there are threats.
Internal validity versus external validity. This
is a classic trade -off. It's like a seesaw.
Internal validity is your confidence that the
independent variable really caused the change
in the dependent variable. It's high in a controlled
lab setting where you can eliminate all other
explanations. But the more controlled your lab
is, the less it looks like the real world. Exactly.
Which lowers your external validity, the degree
to which your findings apply or generalize to
the real world. A field study has high external
validity but low internal validity because of
all the chaos of real life. And there are specific
threats to internal validity. The list has history.
An outside event that happens during your study
and affects the results. You're doing a study
on anxiety and a sudden stock market crash happens
in the middle of it. That crash is a history
threat. Maturation. People just grow or change
naturally over time. If you're studying a reading
program for first graders over a year, they're
going to get better at reading anyway just because
they're getting older. That's maturation. People
dropping out of your study. And it's usually
not random. The people who drop out might be
different from those who stay, which biases your
results. And regression toward the mean. This
one's tricky. It just means that extreme scores
tend to get less extreme over time. If you score
super high on a test, you'll probably score a
little lower the next time. If you score super
low, you'll probably score a little higher. Extreme
scores have nowhere to go but back toward the
average. Okay, last section. My brain is almost
at capacity. Part 6. Making sense of the data.
Statistics. Don't panic. We just need the basics.
First, descriptive statistics. These just describe
or summarize your data. This is your measures
of central tendency. Mean, median, and mode.
Right. Mean is the mathematical average. Mode
is the most frequent score. And median is the
one right in the middle. Why do we need the median
if we have the mean? It's good for skewed data,
like income. If Bill Gates walks into a bar with
10 regular people, the mean income skyrockets
to millions of dollars. But that doesn't really
describe the bar. The median income, the guy
in the middle, stays pretty much the same. It
gives a better picture. OK. And then there's
variability, how spread out the scores are. Standard
deviation. Right. Now for the hard part, inferential
statistics. This is where we make inferences
from our sample back to the population. This
is for decision making. And it all starts with
the null hypothesis. The assumption of no effect.
It's the boring default state of the world. This
new drug does nothing. There is no difference
between these two groups. In the legal system,
it's innocent until proven guilty. In science,
it's no effect until proven otherwise. And the
whole goal of the experiment is to find enough
evidence to reject the null hypothesis. Exactly.
And if we can, we say the result is statistically
significant. But we can make mistakes. The sheet
lists type I and type II errors. Right. A type
I error is a false positive. It's when you reject
the null hypothesis when you shouldn't have.
You conclude the drug works, but it actually
doesn't. You found an effect that isn't really
there. So like convicting an innocent person.
Perfect analogy and a type 2 error is a false
negative It's when you fail to reject the null
when you should have the drug actually works,
but your study missed it You fail to find a real
effect letting a guilty person go free exactly
Wow We did it. That is the entire list. My brain
is full, but I feel like I actually understand
the logic now. It's not just a list of words.
It's a toolkit. That's what's important. These
aren't just terms for the exam tomorrow. They
are tools for critical thinking for the rest
of your life. Right. Because once you leave this
class, you're going to be bombarded with claims
online, on the news. This superfood cures cancer.
Video games cause violence. Understanding methods
allows you to ask the right questions. What was
the sample? Was it just a correlation? How do
they operationally define violence? Exactly.
It's about being a skeptical, informed consumer
of information instead of just accepting everything
you hear. Which brings up a final thought. The
text mentioned the Facebook emotional contagion
study where they manipulated people's news feeds
to see if they could make them happier or sadder
without their consent. It was a huge ethical
controversy. It just makes you think. With social
media and big data, we're all participants in
massive, unconsented experiments every single
day. Every A -B test on a website, every algorithm
change, are we okay with that? That is a deep
and troubling question. Something to mull over
after the exam. For sure. Okay, well, good luck
on the exam. You're gonna crush it. We're both
gonna crush it. See you there.