DATA MANAGEMENT & STATISTICS

Data

Raw facts or information that become meaningful when organized

Qualitative Data

Non-numerical data such as descriptions or categories

Quantitative Data

Numerical data that can be measured or counted

Data Management

Process of organizing

Importance of Data Management

Ensures accuracy

Census

Collection of data from all members of a population

Sample Survey

Collection of data from a subset of a population

Experiment

A study with controlled variables to observe their effects

Observational Study

A study where variables are observed without control or manipulation

Well-Designed Survey

A survey that is representative

Sampling Frame

List of population elements from which a sample is drawn

Nonprobability Sampling

Sampling where some elements have no chance of selection

Convenience Sampling

Selecting subjects that are easiest to reach

Quota Sampling

Selecting subjects based on specific proportions

Probability Sampling

Sampling where each element has a known chance of selection

Simple Random Sampling

A method where every sample has equal chance of selection

Systematic Sampling

Selecting every nth element from a list

Stratified Sampling

Dividing population into groups and sampling each group

Cluster Sampling

Selecting groups or clusters and sampling within them

Matched Sampling

Pairing subjects based on similarities for comparison

Control Group

A group that does not receive treatment in an experiment

Experimental Group

A group that receives treatment in an experiment

Random Assignment

Randomly assigning subjects to groups to avoid bias

Replication

Repeating an experiment to ensure consistency of results

Confounding Variable

An external factor that affects both dependent and independent variables

Placebo

A fake treatment with no active ingredients

Placebo Effect

Improvement caused by belief in treatment rather than treatment itself

Blinding

Hiding treatment information from participants to avoid bias

Blocking

Grouping similar subjects to reduce variability

Completely Randomized Design

Experimental design with random assignment and no grouping

Randomized Block Design

Experimental design where subjects are grouped before random assignment

Matched Pairs Design

Experimental design using pairs of similar subjects

Chi-Square Test

A statistical test used to compare observed and expected frequencies

Chi-Square Statistic

A value measuring difference between observed and expected data

Observed Frequency

Actual data collected in a study

Expected Frequency

Theoretical or predicted data values

Goodness of Fit Test

Checks if observed data matches expected distribution

Test of Independence

Checks if two variables are related

Null Hypothesis H0

Statement that there is no effect or relationship

Alternative Hypothesis Ha

Statement that there is an effect or relationship

Significance Level Alpha

Threshold for deciding whether to reject the null hypothesis

Degrees of Freedom

Number of values that can vary in a calculation

Chi-Square Formula

(O - E)^2 divided by E summed over all categories

Chi-Square Table

A reference table used to find critical values

Critical Value

Threshold value used to decide whether to reject H0

Reject Null Hypothesis

Decision made when test statistic exceeds critical value

Fail to Reject Null Hypothesis

Decision made when test statistic does not exceed critical value

Assumption Random Sample

Data must be collected randomly

Assumption Independence

Each observation must be independent

Assumption Expected Count

Each expected frequency must be at least 5