Psych Stats Exam 2

Descriptive Statistics

set of statistical tools used to summarize, describe, and present data in a
meaningful way

Mean

The sum of all data points divided by the number of data points. It gives a
central value of the data.

Median

The middle value when data points are arranged in ascending or descending order

Mode

The most frequently occurring value(s) in a dataset

Range

The difference between the maximum and minimum values in the dataset

Variance

The average squared deviation from the mean. It measures how much the data points differ from the meaning

Standard Deviation

The square root of the variance. It provides a measure of the average
distance of each data point from the mean

Null Hypothesis

Definition: states that there is no effect or no difference. It is a statement
of no change, no effect, or no relationship.

Purpose: It serves as the default or starting assumption that the researcher seeks to assess

Alternative Hypothesis

Definition: states that there is an effect, a difference, or a relationship. It is what you want to prove.
Purpose: It represents the opposite of the null hypothesis and is accepted if the null hypothesis is rejected.

Significance Level

A significance level (alpha, often 0.05) is chosen to determine the
threshold for rejecting the null hypothesis

One-tailed Test

Tests for the possibility of the relationship in one specific direction

Two-tailed Test

Tests for the possibility of the relationship in both directions

Chi-Square Test of Independence

Purpose: To determine if there is a significant association between two categorical variables

Degrees of Freedom

the number of values in the final calculation of a statistic that are free to vary and will not change the test results

Alpha Value (a)

Definition: The alpha value is the threshold for significance in a hypothesis test. Commonly used alpha values are 0.05 and 0.01 in social sciences.
Purpose: It sets the standard for how strong the evidence must be to reject the null hypothesis.

P-value

the probability of obtaining test results at least as extreme as the
observed results, assuming the null hypothesis is true. It measures the evidence against the null hypothesis

p>a= NOT SIGNIFICANT

p<a= SIGNIFICANT

Type I error

Occurs when the null hypothesis is true, but we incorrectly reject it. Also known as a "false positive" or "false alarm." We conclude that there is an effect or difference when there is not.

Type II Error

occurs when the null hypothesis is false, but we fail to reject it. Also known as a "false negative" or "miss." We conclude that there is no effect or difference when there is

Phi- Coefficient

is a measure of association for two binary categorical variables. The Phi coefficient ranges from -1 to 1, where 0 indicates no association, and values close to -1 or 1 indicate a strong association

Cramer’s V

a measure of association for two nominal variables, and it is a
generalization of the Phi coefficient. ranges from 0 to 1, where 0 indicates no association, and values closer to 1 indicate a stronger association

Independent Samples T-test

Compares the means of two independent groups

Categorical independent variable, continuous dependent variable

Need to be independent of each other

Positive Correlation

Indicates that as one variable increases, the other variable also increases

Negative Correlation

Indicates that as one variable increases, the other variable decreases

Correlation Strength

Values close to +1 or -1 indicate a strong relationship between
the variables. Values close to 0 indicate a weak relationship between the
variables

Regression

a statistical method used to examine the relationships between variables. It helps
understanding how the dependent variable changes as one or more independent variables change

Internal Validity

The extent to which one variable is responsible for the change in another

External Validity

The extent to which the results of a study can be generalized. As internal validity increases, this validity decreases

3 Claims

Frequency, association, and causal

Criteria for Causal Claim

covariance, temporal precedence, and internal validity

conceptual variable

a variable that represents an abstract concept or idea; no direct way to measure the variable

Operational Variable

specifies how a conceptual variable will be measured or manipulated in a study.

Construct validity

how well the variables in a study are measured or manipulated

Statistical Validity

how well the numbers support the claim

Belmont Report 3 guiding principles

respect for persons, beneficence (has to be beneficial for society), and justice

APA Ethics

like belmont report, but added principle of fidelity and responsibility, and the principle of integrity

Questionable Research Practices

underreporting null results, p-hacking (messing with data to find significant result), and harking (changing hypothesis after study)

Theory Testing Mode

tests a theory, does not generalize, external validity is not important

Generalizability mode

representative of the population, applying to the population, external validity is important

Meta-analysis

statistically averages many studies’ findings to quantify if an effect exists

Good Research Practices

pre-registration and open science

Unethical Research Studies

Tuskegee Syphillis Study and Milgram’s obedience experiment

Threats to internal validity

confounds, selection effects, and order effects

Independent-groups design

pretest/posttest design

posttest only

random assignment or matched groups

Within groups Design

repeated measures design

concurrent measures design

Normal Distribution

mean, median, and mode located at the center

Frequency

number of times a category response occurs

Relative Frequency

proportion of times a response occurs in a category

Z-score

How many standard deviations is a data point from the mean