Data Management and Collection Methods

The process of organizing, storing, and using data effectively.

Raw information or facts that can become useful when organized.

Gathering data from every member of a population.

Collecting data from a smaller group representing a larger population.

A study with controlled variables to see how changes affect outcomes.

A study observing real-world events and collecting data without intervention.

The accuracy and reliability of the data gathered. Important for analysis.

The ability to repeat an experiment and get similar results.

A probability sampling method where every nth item from a list is selected, starting with a randomly chosen first item.

Subgroups or categories within a population, used in stratified sampling.

Sampling method ensuring every member of the population has a known, non-zero chance of selection

Simple Random Sampling

Recurring patterns or cycles in a list or population that can bias systematic sampling.

Sampling method that divides a population into distinct groups (strata), then selects a sample from each stratum.

In systematic sampling, different sets of the same sample size do not always have the same probability of being chosen.

A list or database of all the members in the population that researcher intend to study

Dividing a population into groups (strata) and taking a sample from each.

The differences within each subgroup (strata) of the population.

Selecting groups (clusters) of individuals, then sampling individuals within those clusters.

Choosing similar pairs or using the same subject multiple times, under different conditions for comparison.

A study that clearly outlines its purpose, treatment comparison, and expected variability.

The changes observed from applying a treatment in an experiment.

A pre-existing, known, treatment used in experiments as a benchmark for comparison.

The variation in results that is not due to the treatment but might be due to other factors.

An extraneous variable that correlates with both the independent and dependent variables, potentially leading to a false causal relationship.

Improvement in a medical condition due to the expectation of treatment, even if the treatment itself is harmless.

A technique in research where participants do NOT know which treatment group they belong to (or receive) to reduce bias.

Grouping experimental units into similar blocks to control for an extraneous variable that is NOT the main factor being tested.

Experiment design where participants are randomly assigned to different treatment groups to examine the effect of only one primary factor.

Mistakenly concluding there's a relationship between variables when there isn't.

The variable that is manipulated or changed in an experiment to observe its effect.

The variable that is measured or observed to see how it changes in response to the independent variable.

An experiment where levels of a factor are randomly assigned to experimental units.

A collection of completely randomized experiments within blocks of the total experiment.

A special case of randomized block design where blocks have two elements.

Used to compare observed and expected frequencies for one or more categories.

A chi-square test to see if sample data fits a population.

A chi-square test on contingency tables to find if variables are related.

A value used to assess the difference between observed and expected frequencies in a chi-square test.

Random sample, independent observations, and no expected count less than 5.

A calculation formula used to evaluate if there's a relationship or association between two variables. It's derived by comparing observed (O) and expected (E) frequencies in contingency tables.

The actual number of times an outcome or combination of outcomes occurs in data.

The predicted number of times an outcome or combination of outcomes occurs if there is no association between the variables being tested.

A statistical measure used to assess relationships between categorical variables. A large value indicates a strong association between the variables.The sum of (O-E)²/E.

A parameter determining the critical value of the 𝜒² distribution. Usually the number of categories minus one.

The threshold value of the test statistic (𝜒²) that defines when we have enough evidence to reject the null hypothesis under a predetermined level of significance.

The assumption that there is no relationship or association between the variables being tested.

Deciding, based on the statistical test, that the data strongly suggests the relationship between the variables is more likely true than not.