Unit 0

Cultural Norms

Shared rules or behaviors in a society.

Example: In Japan, people bow instead of shaking hands.

Confirmation Bias

Looking for info that supports beliefs and ignoring the rest.

Example: A person against vaccines only reads articles that say vaccines are harmful.

Hindsight Bias

Thinking "I knew it all along" after something happens.

Example: After a sports team loses, saying, “I knew they would lose.”

Overconfidence

• Being more sure about something than is actually justified.

• Example: Thinking you can finish a project in one night but taking three days.

Research Methods :Experimental

• A study where variables are controlled to find cause and effect.

• Example: Testing if sleep affects test scores by controlling sleep hours.

Research Methods :Naturalistic Observ

• Watching behavior in a natural setting without interference.

• Example: Watching kids on a playground to study aggression.

Research Methods :Case Study

• In-depth research on one person or group.

• Example: Studying one person with rare memory loss.

Research Methods :Correlation

Finding a relationship between two things but not proving cause.

Example: More ice cream sales and more shark attacks—but heat causes both!

Research Methods :Meta-analysis

• Combining results from multiple studies to find a trend.

• Example: Looking at 50 studies on caffeine and memory to see the overall effect.

Hypothesis

• A testable prediction.

• Example: "People who sleep 8 hours score higher on tests than those who sleep 4 hours."

Falsifiability

• The ability to prove a hypothesis wrong.

• Example: "All swans are white" can be proven false if one black swan is found.

Operational Definitions

• Defining variables in measurable terms.

• Example: "Happiness" = number of smiles per hour.

Independent Variable

• The factor changed in the experiment.

• Example: Amount of sleep in a study on test scores.

Dependent Variable

• The outcome being measured.

• Example: Test scores in a sleep study.

Confounding Variables

• Extra factors that could affect results.

• Example: If students with more sleep also eat healthier, diet could affect test scores.

Sample

• The group tested in a study.

• Example: 100 students chosen from a school.

Population

• The entire group researchers want to study.

• Example: All high school students, if studying teenage sleep habits.

Representative Sample

• A sample that reflects the whole population.

• Example: If 50% of a school is female, the sample should be 50% female.

Random Sampling

• Everyone has an equal chance to be in the study.

• Example: Drawing names from a hat.

Convenience Sampling

• Using easy-to-reach people.

• Example: Surveying only students in your own school.

Sampling Bias

• When the sample doesn't fairly represent the population.

• Example: Only surveying athletes about school lunch quality.

Generalizability

• How well results apply to others outside the study.

• Example: A study on one small town might not apply to all cities.

Experimental Group

• The group that gets the treatment.

• Example: In a sleep study, the group that sleeps 8 hours.

Control Group

• The group that doesn’t get the treatment.

• Example: In a sleep study, the group that gets 4 hours of sleep.

Placebo

• A fake treatment that has no real effect.

• Example: A sugar pill instead of real medicine.

Single-Blind

• Participants don’t know which group they’re in.

• Example: Students in a sleep study don’t know if they got real or fake sleep tracking.

Double Blind

• Both researchers and participants don’t know who is in which group.

• Example: Neither doctors nor patients know who got real medicine in a drug study.

Social Desirability Bias

• Giving answers to look good instead of being honest.

• Example: Saying you exercise more than you really do.

Qualitative

• Data in words, not numbers.

• Example: Interviews on how people feel about stress.

Quantative

• Data in numbers.

• Example: Counting how many times people check their phones per day.

Peer Review

• Other experts check research before it's published.

• Example: Scientists reviewing a study before it appears in a journal.

Replication

• Repeating a study to see if results are the same.

• Example: Testing a memory trick on different groups to check consistency.

Institutional Review

• A board that checks if research is ethical.

• Example: Approving a study that uses human participants.

Informed Consent

• Participants must know about the study and agree to join.

• Example: Signing a form before an experiment.

Informed Assent

• A child agreeing to participate, with parental approval.

• Example: A 10-year-old saying yes to a study on playtime habits.

Protection from Harm

• Researchers can’t harm participants.

• Example: No extreme stress or danger in an experiment.

Confidentiality

• Keeping participants' info private.

• Example: Hiding names in a depression study.

Deception

• Sometimes researchers mislead participants, but only when necessary.

• Example: Not telling participants the true purpose of a memory test.

Debriefing

• Explaining everything after the study ends.

• Example: Telling participants the real purpose of a deception study.

Measures of Central Tendency

• Mean, median, and mode—ways to find the "center" of data.

Mean

• The average.

• Example: (5+7+8)/3 = 6.67

Median

• The middle number in a set.

• Example: In 3, 5, 7, 9, 11 → median = 7

Mode

• The most common number.

• Example: In 2, 2, 3, 4, 2 → mode = 2

Range

• The difference between the highest and lowest numbers.

• Example: In 3, 5, 9 → range = 9-3 = 6

Normal Curve

• A bell-shaped curve where most scores are in the middle.

• Example: IQ scores, where most people score around 100.

Standard Deviation

• Measures how spread out the numbers are.

• Example: A small SD means scores are close together, a large SD means they vary a lot.

Percentile Rank

• Tells how a score compares to others.

• Example: Scoring in the 90th percentile means you did better than 90% of people.

Regression Toward the Mean

• Extreme scores tend to move closer to average over time.

• Example: A team that wins every game one season may not be as dominant next season.

Scatterplot

• A graph that shows relationships between two variables.

• Example: A graph of hours studied vs. test scores.

Correlation Coefficient

• A number from -1 to 1 showing how strong a relationship is.

• Example: 0.8 means a strong positive correlation, -0.2 means a weak negative correlation.

Statistical Significance

• How likely results happened by chance.

• Example: A p-value of less than 0.05 means the results are probably real, not random.

P value

The p-value tells us how likely it is that the results of an experiment happened just by chance.

In psychology, a p-value below 0.05 (p < 0.05) is considered statistically significant, meaning the results are unlikely due to random chance.

Example:

A psychologist tests if sleep affects test scores.

If p = 0.03, it means there’s only a 3% chance the results happened randomly. The study is statistically significant (p < 0.05), so sleep likely impacts test scores.

If p = 0.10, it means there’s a 10% chance the results are just random. Since p > 0.05, the results are not statistically significant, meaning we can't confidently say sleep improves scores.