Psychology and Research Methods: Key Concepts and Definitions

A set of vocabulary flashcards defining key concepts in psychology and research methodology, including experimental controls, statistical measures, and ethical principles.

Psychology

The scientific study of behavior and mental processes — it uses research, not just opinion, to explain how and why we think, feel, and act

Critical Thinking

Thinking that doesn't just accept claims at face value — it examines assumptions, weighs evidence, and considers other explanations before drawing a conclusion

Hindsight Bias

The "I-knew-it-all-along" effect: once you find out how something turned out, it feels like it was obvious from the start, even though you couldn't have predicted it beforehand

Overconfidence

Being more sure that your judgments, predictions, or knowledge are correct than the evidence actually supports

Perceiving Order in Random Events

Our brain's habit of finding patterns or streaks in things that are actually just chance (like feeling a coin is "due" to land heads after several tails)

Peer Reviewers

Other experts in the field who evaluate a study's methods and conclusions before it gets published, checking for flaws or unsupported claims

Theory

A well-tested explanation that ties together many observations using a set of principles, and lets scientists predict future behavior or events (bigger and better-supported than a hypothesis)

Hypothesis

A specific, testable prediction that comes from a theory — basically an "if this, then that" statement a study is designed to check

Falsifiable

A hypothesis is falsifiable if it's specific enough that evidence COULD prove it wrong; if nothing could ever disprove a claim, it isn't scientific

Operational Definition

A precise, measurable description of a variable — instead of saying "measure happiness," you'd say "measure the number of times someone smiles in $10$ minutes," so anyone could repeat the study the same way

Replication

Redoing a study, often with different people or in a different setting, to see if you get the same results — this is how science checks that a finding is real and not a fluke

Case Study

An in-depth look at one person or small group over time; great for generating ideas and detail, but risky to generalize since one case might not represent everyone

Naturalistic Observation

Watching and recording behavior as it happens in real life, without interfering — you learn what people/animals actually do, but not why (no cause-and-effect conclusions)

Survey

A method that asks a sample of people to report their own attitudes, beliefs, or behaviors, usually through questions — quick way to get info from a lot of people, but depends on honesty

Social Desirability Bias

The tendency for people to shade their survey answers to look better, healthier, or more socially acceptable than they truly feel or behave

Self-Report Bias

Errors that creep into data because people misjudge, misremember, or exaggerate when describing their own thoughts or behaviors

Population

The entire group a researcher wants to draw conclusions about (e.g., "all U.S. teenagers"), even though they usually only study a smaller slice of it

Sample

The smaller subset of the population that's actually studied, used to make inferences about the whole group

Sampling Bias

When the way a sample is selected causes it to not represent the population well, skewing results (e.g., only surveying people at the mall on a Tuesday afternoon)

Random Sample

A sample where every individual in the population has an equal chance of being chosen — the gold standard for making a sample actually representative

Convenience Sampling

Selecting whoever is easiest to reach rather than randomly — fast and cheap, but often unrepresentative and prone to bias

Representative Sample

A sample whose characteristics (age, gender, background, etc.) closely mirror those of the population it's drawn from

Generalizability

How well the results from a study's sample can be applied to the larger population — depends heavily on whether the sample was representative

Correlation

A statistical measure of how strongly two variables rise and fall together — it tells you they're related, but never by itself that one causes the other

Positive Correlation

Two variables move in the same direction — as one goes up, the other tends to go up too (e.g., more study time, higher test scores)

Negative Correlation

Two variables move in opposite directions — as one goes up, the other tends to go down (e.g., more stress, less sleep)

Correlation Coefficient

A number between $-1$ and $+1$ that shows both the strength and direction of a relationship between two variables — closer to $!!\pm 1!!$ means a stronger relationship, closer to $0$ means a weaker one

Variable

Anything that can change or vary and be measured in a study (age, mood, reaction time, etc.)

Scatterplot

A graph with dots representing pairs of scores for two variables — the pattern of dots shows whether (and how) the variables are related

Illusory Correlation

Perceiving a relationship between two things that either doesn't really exist or is much weaker than it seems (e.g., thinking your phone is "listening" because an ad matched something you talked about once)

Directionality Problem

Even when two variables are correlated, you can't tell which one is causing the other — either could be the cause (e.g., does poor sleep cause depression, or does depression cause poor sleep?)

Third Variable Problem

A hidden, unmeasured factor might be driving both variables in a correlation, making them look related when neither actually causes the other (e.g., ice cream sales and drownings both rise in summer — heat is the third variable)

Regression Toward the Mean

After an unusually extreme score (very high or very low), the next measurement tends to land closer to average — not because anything changed, just statistically likely (e.g., a rookie's amazing first game is often followed by a more "normal" one)

Experiment

A research method where the researcher deliberately manipulates one factor and controls others, to figure out cause and effect

Non-Experimental Methods

Research approaches (case study, correlation, naturalistic observation, survey) that describe or relate behaviors but don't manipulate variables, so they can't establish cause and effect

Experimental Group

The group in an experiment that receives the treatment or condition being tested

Control Group

The group in an experiment that does NOT receive the treatment, used as a baseline for comparison

Independent Variable

The factor the experimenter deliberately changes or manipulates, to see what effect it has

Dependent Variable

The outcome that's measured in an experiment — it "depends on" what happens to the independent variable

Random Assignment

Using chance to place participants into the experimental or control group, which helps make the groups similar before the study begins so differences in results can be credited to the treatment

Single-Blind Procedure

A study where participants don't know which group (treatment or control) they're in, but the researchers do

Double-Blind Procedure

A study where neither the participants nor the researchers interacting with them know who's in which group — this controls for both the placebo effect and experimenter bias

Placebo

A fake treatment (like a sugar pill) with no active ingredient, given to the control group so it can be compared with the real treatment

Placebo Effect

Experiencing real improvement or change simply because you expect a treatment to work, even though you didn't actually get the real treatment

Confounding Variable

An outside factor, other than the independent variable, that could also explain differences in the results — a threat to drawing a clean cause-and-effect conclusion

Experimenter Bias

When a researcher's own expectations unintentionally influence how they treat participants or interpret results

Validity

Whether a test, measure, or study actually measures what it claims to measure

Reliability

Whether a test or measure produces consistent results if you repeat it

Quantitative Research

Research that collects numerical data that can be measured and statistically analyzed (e.g., test scores, reaction times)

Likert Scale

A rating scale (e.g., "strongly disagree" to "strongly agree," often $1$-$5$ or $1$-$7$) used to quantify attitudes or opinions in surveys

Qualitative Research

Research that collects non-numerical data — like descriptions, themes, or quotes — to explore experiences or meanings in depth

Structured Interview

An interview where every participant is asked the same set of questions in the same order, making responses easier to compare

Institutional Review Board (IRB)

A committee at a research institution that reviews studies before they start, to make sure they meet ethical standards and protect participants

Informed Consent

Giving participants enough information about a study up front so they can knowingly and voluntarily agree to take part

Informed Assent

Similar to informed consent, but for participants (usually minors) who can't legally give full consent themselves — they agree to participate after being told what's involved, alongside a parent/guardian's consent

Protect from Harm

The ethical principle that researchers must avoid putting participants at risk of physical or psychological harm beyond normal everyday risk

Confidentiality

The ethical obligation to keep participants' data and identities private

Debriefing

After a study ends, explaining its true purpose (and revealing any deception used) to participants

Research Confederates

People who appear to be regular participants in a study but are secretly working with the researcher, often to create a specific social situation to observe reactions to

Descriptive Statistics

Numbers that summarize and describe a data set (like averages or percentages) without drawing broader conclusions beyond that data

Histogram/Bar Graph

A chart using bars to show how frequently different values or categories occur in a data set

Measures of Central Tendency

The general term for the three ways of describing the "center" or typical score in a data set: mean, median, and mode

Mode

The value that appears most often in a data set

Mean

The mathematical average of a data set — add up all values and divide by how many there are

Median

The middle value in a data set when all scores are lined up from lowest to highest

Percentile Rank

The percentage of scores in a data set that fall at or below a particular score (e.g., scoring in the $90\text{th}$ percentile means you scored higher than $90\%$ of people)

Percentage

A way of expressing a value as a portion out of $100$

Bimodal Distribution

A data set with two distinct "peaks" or most-frequent values, rather than just one

Skewed Distribution

A data set that isn't symmetrical — most scores cluster on one side with a "tail" stretching out on the other

Positive/Right-Tailed Skew

A distribution where most scores are low but a few unusually high scores stretch the tail out to the right

Negative/Left-Tailed Skew

A distribution where most scores are high but a few unusually low scores stretch the tail out to the left

Measures of Variation

The general term for statistics (like range and standard deviation) that describe how spread out or spread apart scores in a data set are

Range

The difference between the highest and lowest scores in a data set — a quick but rough measure of spread

Standard Deviation

A number showing, on average, how far scores in a data set stray from the mean — a low value means scores cluster tightly around the mean, a high value means they're spread out

Normal Curve

The bell-shaped pattern many natural traits follow, where most scores cluster near the mean and fewer scores appear as you move toward either extreme

Inferential Statistics

Statistical methods used to determine whether results from a sample can be generalized (inferred) to apply to the larger population

Meta-Analysis

A method that statistically combines the results of many separate studies on the same topic to get a more powerful, reliable overall conclusion

Statistical Significance

A statistical measure of how likely it is that a study's result happened just by chance rather than reflecting a real effect — a low likelihood of chance means the result is "statistically significant"

Effect Size

A number indicating how large or meaningful a study's result actually is, separate from whether it's statistically significant — a result can be significant but still small in real-world impact