Psy 201 Lec 1 terms & things to remember

Population

the entire collection of individuals or objects about which info is desired.

Sample

a subset of population from which info is collected.

Descriptive stats

the type of stats used to organize and summarize the data.

inferential stats

the type of stats used to make inference about the population from the sample.

Data

a collection of observations on one or more variables.

Variable

a characteristic whose value may change from one observation to another.

Numerical data

data whose observations are numerical.

Categorical data

data whose observations are categorical.

Univariate data set

 a set of data whose observations vary only in one characteristics.

Multivariate data set

a set of data whose observations vary in multiple characteristics.

Discrete numerical variable

a numerical variable whose possible values are isolated and limited points on the number line.

Continuous numerical variable

 a numerical variable whose possible values can be anywhere on the number line.

Frequency

the frequency of a category is the number of times the particular category is observed within a dataset.

Relative frequency

the relative frequency of a category is the proportion of the observations that belongs to the particular category.

Observational study

a study in which characteristics of a sample selected from one or more existing populations are observed in order to draw some conclusion about the population or the difference in the populations with regard to the characteristics.

Selection or sampling bias

the tendency for the sample to differ from the  population as a result of systemic exclusion of some part of the population.

Measurement or response bias

the tendency for the sample to differ from the population due to specifics of methodologies employed to measure the characteristics of interest.

Nonresponse bias

the tendency for the sample to differ from the population because a particular subset of samples did not (or choose not to) contribute to the measurements of the characteristics of interest.

Simple random sample size of n

 a sample size of n selected by ensuring that every different possible sample of size n has the same chance of being selected.

Sampling without replacement

once an individual form the population is selected to be included in a sample, the individual cannot be selected again in the sampling process. This ensures that a sample with the size = n will include n different individuals from the population.

Sampling with replacement

After an individual from the population is selected to be included in a sample, the individual is placed back in the population and can be selected again in the sampling process. Thus, any given individual has a chance of being selected into the sample multiple times.

Stratified random sampling

a way of picking people for a study by first dividing them into groups based on something they have in common like age, gender, or income, and then randomly choosing people form each group. This helps make sure every group is fairly represented in the results.

Cluster sampling

 a way of picking people for a study by first dividing the population into groups (called clusters) like school, neighborhoods, or cities, and then randomly choosing some of those groups. Everyone In the selected groups is included in the study.

Systematic sampling

selecting people at regular intervals from a list.

Convenience sampling

 involves picking people who are easy to reach, like people who are nearby, available, or willing to participate (no random sampling).

Experimental study

A type of research where the researcher actively changes something (the independent variable) to see how it affects something ( the dependent variable). Ex: giving one group of students extra tutoring and comparing their test scores to a group that did not get tutoring.

Explanatory variable

the variable that the researcher changes or controls to see what effect it has. (independent variable).

Response variable

the variable that the researcher measures to see if it changed because of the explanatory variables (dependent variable).

Experimental conditions

 the different setups or groups in the experiment. Ex: one group gets tutoring (treatment group) and one doesn’t (control group). Each group is an experimental condition.

Extraneous variables

the extra factors that might affect the results but aren't the focus of the study and if not controlled, can mess up the results of the experiment/study. Ex: if some students slept poorly before the test, that could affect their scores regardless of tutoring.

What is a good experiment?

A good experiment makes sure that only the thing you're testing (explanatory variable effects the result (response variable), and nothing else gets in the way.

The 4 BFFs to design a good experiments are:

• Direct control: holding the extraneous variables constant across experimental conditions.

• Random assignment: assigning samples randomly across experimental conditions to even out contribution of the extraneous variables.

• Blocking: using the extraneous variables to create groups that are evenly assigned across experimental conditions.

•  replication: repeating the experiment multiple times to ensure that the observed effect is reliable and not due to some unusual characterises to a particular data set.