What is Correlational Research? Finding Relationships Between Variables

Summary

This video explores the common misconception that correlation implies causation, using the example of a 2003 study claiming that eating pizza reduces cancer risk. It highlights the importance of understanding the difference between correlation and causation, cautioning against jumping to conclusions based on superficial links. The video discusses how other factors, like a Mediterranean diet, could be the real reason behind the observed health benefits, and explains the role of third variables in misleading correlations. It also touches on the illusory correlations found in superstitions and the statistical methods used to measure correlations.

Takeaways

• 🍕 The 2003 Italian study suggesting that eating pizza can reduce the risk of certain cancers was widely publicized but does not imply a direct causal relationship.
• 🧠 Correlation does not imply causation, a common misconception where two related variables are incorrectly assumed to have a cause-and-effect link.
• 🌿 The Mediterranean diet, rich in olive oil, fish, grains, fruits, and tomatoes, is more likely the factor contributing to cancer prevention rather than pizza alone.
• 🔍 The third variable problem cautions against assuming a direct link between two variables when an unknown third variable might be the actual cause of the observed relationship.
• 🎮 The example of violent video games and aggressive behavior illustrates the complexity of establishing causation, where other factors like family violence might be the real cause.
• 🍬 Illusory correlations, like the perceived link between eating candy and hyperactivity, are relationships that people believe exist but are not supported by evidence.
• 🏃‍♂️ Superstitions in sports, such as rituals performed by athletes, are often based on illusory correlations and not actual causation.
• 📊 Correlation coefficients, like Pearson's R, measure the strength and direction of a relationship between two variables, with values ranging from -1.0 to +1.0.
• ➡️ Positive correlations indicate that as one variable increases, the other also increases, while negative correlations suggest an inverse relationship.
• 📈 Scatter plots are used to graphically represent correlations, showing the direction and strength of the relationship through the pattern of data points.

Q & A

What was the claim made by Italian researchers in their 2003 scientific article about pizza?

The Italian researchers claimed that eating pizza can reduce the risk of certain cancers.

Why did the study's findings about pizza and cancer risk receive widespread attention?

The study's findings received widespread attention because it was a surprising and counterintuitive claim that a food often considered unhealthy, like pizza, could have health benefits such as reducing cancer risk.

What is the main caution given by other scientists and the authors of the study regarding the link between pizza and cancer risk?

Other scientists and the authors caution against assuming a causal relationship between pizza consumption and reduced cancer risk, emphasizing that correlation does not imply causation.

What is the Mediterranean diet, and how is it related to the study on pizza and cancer risk?

The Mediterranean diet is a diet rich in olive oil, fish, grains, fruits, and tomatoes, which is considered healthier and more likely to contribute to fighting cancer than pizza itself. The study's findings might be more related to the Mediterranean diet than to pizza directly.

What is the 'third variable problem' mentioned in the script, and how does it relate to correlations?

The 'third variable problem' refers to a situation where two variables appear to be linked, but there is actually an unknown or third variable that is the real source of the link. This problem reminds us that correlation does not necessarily mean causation.

Can you provide an example of the 'third variable problem' from the script?

An example given in the script is the supposed link between playing violent video games and aggressive behavior. The third variable could be exposure to family violence, which is associated with both increased risk for playing violent video games and youth violence.

What is an 'illusory correlation' as discussed in the script?

An 'illusory correlation' is a perceived relationship between variables that does not actually exist. The script uses the example of athletes believing in superstitions, such as wearing lucky socks to improve performance, even though there is no actual correlation.

Why do psychologists conduct correlational research, and what do they aim to understand?

Psychologists conduct correlational research to understand the relationship between two variables, specifically to see how well one variable predicts the presence or absence of another variable.

What is a correlation coefficient, and how is it used in psychology research?

A correlation coefficient is a statistical measure that quantifies the strength and direction of a correlation between two variables. It is represented by Pearson's R, and its value can range from +1.0 to -1.0, with each extreme indicating a perfect correlation.

How are correlations typically graphed in psychological research, and what do the different elements of the graph represent?

Correlations are typically graphed on scatter plots, where the slope indicates the direction of the correlation, and the scatter of the data points indicates the strength. Each participant or event is represented by a data point, and the closer the points are to the line of best fit, the stronger the correlation.

What is the difference between a positive and a negative correlation, and can you provide examples from the script?

A positive correlation implies that as the value of one variable increases, the value of the other variable also increases, such as time spent on a treadmill and calories burned. A negative correlation implies that as the value of one variable increases, the value of the other variable decreases, such as alcohol consumption and judgment.

Outlines

00:00

🍕 Pizza and Cancer Risk: Debunking Myths

The paragraph discusses a study by Italian researchers in 2003 that suggested eating pizza could reduce the risk of certain cancers. This led to sensational headlines, but the study did not establish a causal relationship. The text emphasizes that correlation does not imply causation, and the actual factor in fighting cancer might be a Mediterranean diet, which includes pizza ingredients like tomatoes. The paragraph also introduces the concept of the third variable problem, where an unknown variable might be the real cause behind the apparent correlation between two variables. Examples include the relationship between violent video games and aggression, and the potential influence of family violence as a third variable. The paragraph concludes by cautioning against illusory correlations, such as the non-existent link between eating candy and hyperactivity.

05:01

📊 Understanding Correlation in Psychology

This paragraph delves into the reasons why psychologists conduct correlational research, aiming to understand the relationship and predictive power between two variables. It explains the correlation coefficient, particularly Pearson's R, which measures the strength and direction of a correlation, ranging from -1.0 to 1.0. The paragraph clarifies that a positive correlation indicates a direct relationship where an increase in one variable is associated with an increase in the other, while a negative correlation suggests an inverse relationship. Examples are provided, such as the positive correlation between time spent on a treadmill and calories burned, and the negative correlation between alcohol consumption and judgment. The paragraph also discusses how correlations are visually represented on scatter plots, with the slope indicating the direction and the scatter indicating the strength of the correlation. The text encourages viewers to pause and check their understanding of these concepts before proceeding.

Keywords

💡Correlation

Correlation refers to a statistical relationship between two variables, indicating how one variable may predict or be related to another. In the video, the concept of correlation is central to understanding how eating pizza was associated with a reduced risk of cancer, although this does not imply causation. The video emphasizes that just because two variables are correlated, it doesn't mean one causes the other.

💡Causation

Causation means that one event is the direct result of another. The video explains that while correlation might show a relationship between two variables, it does not necessarily mean that one causes the other. For example, the study mentioned in the video suggested a correlation between eating pizza and reduced cancer risk, but this does not prove that pizza consumption causes lower cancer risk.

💡Third Variable Problem

The third variable problem occurs when an unaccounted-for variable influences both variables in a study, creating a false impression of a direct relationship between them. In the video, this concept is used to caution against assuming that pizza directly reduces cancer risk, as there may be other factors, such as a Mediterranean diet, that are the actual cause.

💡Illusory Correlation

An illusory correlation is when people perceive a relationship between two variables even when no such relationship exists. The video provides examples such as superstitious rituals in sports, where athletes might believe certain actions improve their performance, despite there being no actual connection.

💡Mediterranean Diet

The Mediterranean diet is a diet rich in olive oil, fish, grains, fruits, and vegetables. The video suggests that the health benefits attributed to eating pizza might actually be due to the Mediterranean diet, which includes ingredients like tomatoes that are also found in pizza. This diet is more likely the cause of reduced cancer risk, not pizza itself.

💡Superstitions

Superstitions are irrational beliefs that certain actions can influence outcomes, despite there being no causal connection. The video uses sports superstitions as an example of illusory correlations, where athletes might perform rituals believing they impact their success, even though this relationship is not based on evidence.

💡Scatter Plot

A scatter plot is a type of graph used to represent the relationship between two variables. In the video, scatter plots are mentioned as a method psychologists use to visualize correlations, where the slope indicates the direction of the relationship and the scatter of data points shows the strength of the correlation.

💡Pearson's R

Pearson's R is a statistical measure of the strength and direction of a correlation between two variables. The video explains that the value of Pearson's R ranges from -1 to 1, with values closer to 1 indicating a strong positive correlation, and values closer to -1 indicating a strong negative correlation.

💡Positive Correlation

A positive correlation occurs when an increase in one variable is associated with an increase in another variable. The video gives examples such as the relationship between time spent on a treadmill and calories burned, where both increase together, illustrating a positive correlation.

💡Negative Correlation

A negative correlation occurs when an increase in one variable is associated with a decrease in another. The video illustrates this with examples like the relationship between alcohol consumption and judgment, where increased drinking is associated with decreased judgment, indicating a negative correlation.

Highlights

Italian researchers in 2003 claimed that eating pizza could reduce the risk of certain cancers.

Pizza, a popular Super Bowl Sunday food, was linked to fighting cancer in sensational headlines.

The study's authors caution against assuming a causal relationship between pizza and reduced cancer risk.

A Mediterranean diet, rich in olive oil, fish, grains, fruits, and tomatoes, is more likely the cause of fighting cancer.

Correlation does not imply causation, a common error in interpreting relationships between variables.

The 'third variable problem' can create an illusion of a link between two variables when a third variable is the actual cause.

Violent video games and aggressive behavior in children may be linked by a third variable, such as exposure to family violence.

Illusory correlations, like between eating candy and hyperactivity, occur when people perceive relationships that do not exist.

Athletes' superstitions often involve rituals believed to correlate with improved performance, despite no actual causation.

Psychologists conduct correlational research to understand how one variable predicts the presence or absence of another.

Correlation coefficients, like Pearson's R, measure the strength and direction of the relationship between variables.

A positive correlation implies that as one variable increases, the other also increases.

A negative correlation indicates that as one variable increases, the other decreases.

Correlations are graphed on scatter plots, where the slope indicates the direction and the scatter indicates the strength of the correlation.

Positive and negative in correlation do not signify good or bad but rather the direction of the relationship.

The strength of a correlation is not about its goodness or badness but about how well one variable predicts another.

Transcripts

in 2003 Italian researchers published a

scientific article claim that eating

pizza can reduce the risk of certain

cancers that's right one of the most

consumed Foods on Super Bowl Sunday

greasy cheesy salty Pizza is linked to

fighting cancer newspaper headlines

online blogs and editorials picked up on

this astonishing study creating

headlines like eating pizza Cuts cancer

risk why Pizza can fight cancer and

pizza reduces cancer risk now you may be

thinking can eating pizza really cut the

risk of cancer is there a causal

relationship between my favorite meat

lovers deep dish and healthy living well

not exactly you see correlation does not

imply causation that is to say if a is

related to B that doesn't necessarily

mean a cause B this is a common error in

our thinking looking deeper into the

research findings there's much more than

meets the

ey in this pyemia episode I explore

correlations how two variables yes even

peace and health are

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related it is very tempting and even

exciting for food lovers to read these

aformentioned headlines and think that

eating pizza can cause a reduction in

cancer risk but other scientists and

even the authors of the study caution

readers to quickly make a cause your

relationship it turns out that a

Mediterranean diet is more likely the

cause of fighting cancer in Healthy

Living a diet that is rich in olive oil

fish grains fruits and yes tomatoes one

of the main ingredients on a

pizza why does a correlation not imply

causation it is common to think that

when two things relate to one another or

appear linked like money and happiness

violent video games and aggressive

behavior and eating breakfast and

success in school that one caused the

other but but there are several reasons

to be cautious for instance maybe

there's a third variable often times two

variables appear to be linked to each

other but in actuality there is another

unknown or third variable that is a real

source of the link this is called the

third variable problem let's take a look

at one of these examples for decades

psychologists have investigated a link

between first-person killing games like

Grand Theft Auto and Call of Duty and

aggressiveness in boys one could argue

that playing violent video games causes

aggressive behavior this argument

supports why politicians in the past

have tried to put an age limit on

purchasing violent video games however

one could also make the argument that a

child who is already aggressive is more

likely to seek out and play violent

video games because it connects to their

personality of course what about a third

variable some Studies have shown that

exposure to Family Violence like spous

and child abuse is associated with youth

violence and increased risk for playing

violent video games

this finding reminds us that we should

not jump to conclusions when

establishing links between two

variables it is also very common for

people to see relationships between

variables when non-exist like eating

candy and hyperactivity this is called

an illusory

correlation let's take a look at a

common example of ubiquitous in sports

superstitions athletes are renowned for

being

superstitious they often develop unusual

rituals to keep hitting streaks alive or

to end terrible slubs for example

anything to get the bad juju off their

back whether wearing the same lucky

socks or eating the same meal before

every game from a psychological

perspective athletes have convinced

themselves that a relationship exists

between performing specific rituals and

Performing well on a field unfortunately

this relationship is merely a

fallacy so why do psychologists conduct

correlational research foremost

psychologists are interested in the

relationship between two variables

specifically how well one variable

predicts the present presence or absence

of another variable psychologist study

relationships in all walks of life like

the relationship between attendance and

GPA money and happiness intelligence and

income and depression and eating habits

notice how the arrows are pointing in

both directions as I stated previously

this is because it's very difficult to

make a cause a relationship between two

variables figuring out how closely two

variables relate to or predict one

another is measured using a statistical

measure called correlation coefficient

this index measures the strength of a

correlation represented by Pearson's R

the value of a correlation can range

from positive 1.0 to negative 1.0 each

being a perfect correlation an R of Zer

means no relationship exists between two

variables a correlation of positive 87

for example will be considered very

strong while a negative .27 will be

considered weak before we move on check

your understanding of the strength of a

correlation take a look at the following

following our values and identify the

strength of each score pause the video

here how' you do think about the

mistakes you made if any and Rewind the

video to clarify any confusion it is

important to note that positive doesn't

mean good and negative doesn't mean bad

but rather positive and negative signify

the direction of the

correlation specifically a positive

correlation implies that one variable

predicts the presence of another

variable in other words as the value of

one variable increases the value of the

other variable increas Es as well for

example there is a positive correlation

between the time you spend on a

treadmill and the number of calories

burned in other words the more you run

the more calories you lose there's also

a positive correlation between the

amount of coffee you drink and level of

alertness the more coffee you drink the

more alert you will be in the classroom

or office conversely a negative

correlation applies that one variable

predicts the absence of another variable

in other words as the value of one

variable increases the value of the

other variable decreases Ines for

example there is a negative correlation

between alcohol consumption and judgment

in other words the more alcohol you

drink the less judgment one has there's

also a negative correlation between the

amount of garlic in your home and

vampires the more garlic K in your

kitchen the less Vampires will step foot

in your house check your understanding

of positive and negative

correlations pause the video here and

brainstorm a few examples before moving

on lastly how do psychologist graph

correlations correlations are graphed on

Scatter Plots like the one seen above

the slope of the scatter plot indicates

the direction of the correlation while

the scatter of the data points indicates

the strength each participant or event

in a study is represented by a DOT or

data point on the scatter plot the more

scattered between data points for

example would mean there's a weak

correlation between two variables the

closer the data points are bunched

together on the line of best fit would

indicate a stronger relationship

before we finish check your

understanding of the correlational

method fill in the blanks to make the

statement accurate pause the video here

before checking your answers if you had

any trouble filling in the blanks run

the video and continue the

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review