AP Psych Unit 1

Vocabulary flashcards covering science practices, research methods, statistical measures, and experimental design in psychology.

Scientific method

An iterative process used to get closer to the truth through observation, hypothesis, experiment, and analysis.

Observation

The process of asking questions and observing things using the 5 senses.

Hypothesis

A possible answer to an observation or an educated guess that allows us to make a prediction.

Prediction

A testable statement that describes what we think the outcome of an experiment will be.

Experiment

A scientific test to observe effects and learn something, serving as the only research method that can establish causation.

Analysis

A mathematical process that shows if there is any data that can accept or reject a hypothesis.

Theory

A hypothesis that stands the test of repeated experiments.

Illusionary truth effect

The tendency for any statement that is repeated frequently to feel like the truth.

Skepticism

Judging whether something is true or not.

Fact

Observable realities obtained from scientific evidence.

Opinion

Subjective, personal judgements, conclusions, or attitudes.

Survey

A list of questions to be answered by participants, which is fast and cheap for reaching a large audience, but cannot establish cause and effect.

Sample

The group of people that a survey is given to.

Population

The group of people that includes sample(s), which researchers generalize based on the sample.

Central tendency

Important characteristics derived from the responses of a survey.

Mode

The most frequent response in a dataset, which is good for non-quantitative surveys.

Median

The response in the middle of a given dataset, such as in $-3, 5, 12, 13, 100$.

Mean

The average of all responses, which is good for quantitative surveys along with standard deviation.

Standard deviation

A measure of how much scores vary from the mean.

Naturalistic observation

Observing behaviour in natural settings while remaining inconspicuous so as not to interfere with human or animal natural habitats.

Case study

A research method where a subject is followed closely for a long period of time to obtain lots of detailed information, though results are not generalizable and consent is required.

Wild boy of Aveyron

A boy aged 11/12 living in the wild for several years who served as a natural experiment into the question of nature and nurture.

Variable

A characteristic that can be measured and can assume different values.

Control group

The comparison group in an experiment that does not receive the variable being tested.

Experimental group

The group in an experiment that receives the variable being tested.

Causation

B happens BECAUSE of A

Correlation

B happens WHEN A happens

Confounding variable

Variable that relates its two effects (rain= sadness, plants growing)

Perceived expertise

When people tend to believe the claims of someone with authority/expertise

Social influence

When people adopt their opinion, revise their beliefs, or change their behaviour as a result of social interactions

Inverse relationship

when one variable increases and the other decreases (negative correlation)

Correlation coefficient

Quantity to calculate correlation

Close to 1 : strongly positively correlated

Close to -1 : strongly negatively correlated

Close to 0 : weakly correlated

The higher the correlation, the prediction is…

Better

Hypothesis states a ___ and ____ relationship

Cause and effect

Independent variable

Cause

Dependant variable

Effect

Operational definition

Description of how a variable is measured (ex. Water in litres)

Reliability (in experiments/tools)

Ability to constantly produce a given result

Validity (tools)

How well a tod measures what its supposed to measuse

Correlational study

Looking whether two variables are related without manipulating either. Finding correlation of data instead of causation

What analysis is good for quantitative data?

statistical analysis

Random sampling

Good way to avoid bias when drawing samples from large populations.

Null hypothesis (statistical analysis)

Original hypothesis

Alternative hypothesis (statistical analysis)

Opposite of original hypothesis

Retraction

Removal of a published study due to serious problems

Falsification

Manipulating data

Fabrication

Making up data

conflict of interest

personal/financial interest that could affect research objectivity.

Statistical analysis

Collecting and analyzing large amounts of data to identify trends and valuable insights

Quantitative data

Data represented numerically

Three classifications of quantitative data?

• categorical data: contains categories/groups (ex. Countries)

• Discrete data: can be counted as whole numbers (ex. Apples)

• Continues data: value in a range (ex. Temperature)

Qualitative data

data representing info and concepts that aren’t represened by numbers

Data coding

Process of transforming raw collected data into a set of meaningful categories that describe essential concepts of the data. (Used for qualitative data)

Descriptive statistics

Used when it's not possible to present all the data in any form that the reader can quickly interpret

Infernal statistics

Discovering property/general pattern about a large group by studying a smaller group and hoping the result generalizes the larger group

Placebo

Fake treatment

What sample size is for for better results?

Bigger

What happens if author of a research/analysis doesn’t give the sample size?

The data can be wrong

Variability

How much data differs

What are the three things needed to come up with a good conclusion from infernal analysis?

Mean, sample size, variability

Null hypothesis (infernal analysis)

When your two samples don’t differ in results (goal in infernal analysis is to prove null hypothesis is wrong)

Blinding (infernal analysis)

When study participants are prevented from knowing info that might influence them result

Alternative hypothesis (infernal analysis)

When two samples get different results

Double-blinding

When both participants and researchers are prevented from knowing info that can affect the research results to prevent bias

p-value

a probability value of getting results this extreme if null hypothesis is true.

• Low p-value = low probability null hypothesis is correct

• Small p-values give us more reason to reject hull hypothesis