DATA MANAGEMENT & STATISTICS
π DATA MANAGEMENT & STATISTICS REVIEWER
(Based on your handouts )
π§ 1. What is Data & Data Management?
πΉ What is Data?
Data = raw facts or information
Can be:
Qualitative β descriptions (e.g., color, opinions)
Quantitative β numbers (e.g., age, height)
π Example:
βBlueβ = qualitative
β18 years oldβ = quantitative
πΉ What is Data Management?
Think of this as alaga system ng data mo.
Organizing data
Checking errors
Preparing for analysis
Storing & documenting data
π‘ In short, ganito lang βyan:
Data management = βfrom gulo β organized β useful infoβ
π₯ Importance (Why it matters)
Ensures accurate conclusions
Makes data reusable (future studies)
Improves efficiency & quality
π§ Quick Memory Trick:
π D-A-T-A
Data organized
Analysis-ready
Trustworthy
Archived
π 2. Methods of Data Collection
πΉ 4 Main Methods:
Method | Meaning | Example |
|---|---|---|
Census | Whole population | National census |
Sample Survey | Subset only | Survey 100 students |
Experiment | With control variables | Testing medicine |
Observational Study | No control | Smoking vs lung cancer |
π‘ Key Insight:
Census = complete but expensive
Sampling = cheaper & faster
π Real-life:
Shopee reviews? Thatβs sampling, not census π
π§ Memory Trick:
π CS-E-O
Census
Sample
Experiment
Observation
π― 3. Surveys & Sampling
πΉ Good Survey = Dapat ganito:
Representative (fair sample)
Random selection
Neutral questions
Controlled bias
π‘ Example:
Bad question β: βDo you agree this product is amazing?β
Good question β
: βHow would you rate this product?β
πΉ Sampling Methods
π΄ Non-Probability Sampling
NOT random
Biased
Examples:
Convenience sampling
Quota sampling
π Example:
Interview mo lang mga friends mo = biased agad π¬
π’ Probability Sampling (Better π₯)
1. Simple Random Sampling (SRS)
Equal chance lahat
2. Systematic Sampling
Every nth item (e.g., every 10th person)
3. Stratified Sampling
Divide into groups (e.g., male/female)
4. Cluster Sampling
Group by location (e.g., barangay)
5. Matched Sampling
Paired comparison (before vs after)
π§ Memory Trick:
π βSi Syempre Study Classes Moβ
Simple
Systematic
Stratified
Cluster
Matched
π§ͺ 4. Experiments (Super Important π₯)
πΉ Key Components
Control Group β walang treatment
Experimental Group β may treatment
Random Assignment β fair distribution
Replication β repeat for accuracy
π‘ Real-life Example:
Testing new skincare:
Group A: gumagamit
Group B: hindi
β Bias & Errors
πΈ Confounding Variable
Hidden factor affecting results
π Example:
Ice cream sales β and drowning β
β‘ Real cause: summer (confounder)
πΈ Placebo Effect
Fake treatment but may effect pa rin
πΈ Blinding
Participants donβt know treatment
πΈ Blocking
Group similar subjects
π Example:
Separate males & females in study
π§ Memory Trick:
π CPBB
Confounding
Placebo
Blinding
Blocking
π§ͺ 5. Experimental Designs
Design | Meaning |
|---|---|
Completely Randomized | Pure random |
Randomized Block | Group then random |
Matched Pairs | Pair subjects |
π‘ In short:
Random = simple
Block = organized
Matched = paired
π 6. Chi-Square Test (Very Exam Favorite π₯)
πΉ Purpose:
To check if:
Data matches expectation OR
Two variables are related
πΉ Formula
Ο2=β(OβE)2E\chi^2 = \sum \frac{(O - E)^2}{E}Ο2=βE(OβE)2β
Where:
O = Observed
E = Expected
πΉ Types of Chi-Square
1. Goodness of Fit
π Checks if data matches expected distribution
2. Test of Independence
π Checks if 2 variables are related
π§ Key Idea:
Small ΟΒ² β good fit (may relationship)
Large ΟΒ² β poor fit (no match)
πΉ Assumptions
Random sample
Independent observations
Expected β₯ 5
πΉ Hypothesis
Test | Hβ | Hβ |
|---|---|---|
Goodness | Matches expected | Not match |
Independence | No relationship | Has relationship |
π₯ Example Insight (from your handout)
π On page 6β7,
If ΟΒ² > critical value β Reject Hβ
Meaning: may difference or relationship
π§ Memory Trick:
π βO-E squared over Eβ
(Just memorize flow ng formula)
π 7. Chi-Square Table (Handout 2)
Used to find critical value
Based on:
Degrees of freedom (df)
Significance level (Ξ±)
π Makikita ito sa table (page 1 of Handout 2)
π‘ Example:
df = 3, Ξ± = 0.05 β critical = 7.815
π§ Quick Tip:
π If computed ΟΒ² > table value β Reject Hβ
β‘ FINAL RECAP (Ultra Simplified)
π If cram mode ka na, eto na lang tandaan mo:
π§ Data Management
Organize β Clean β Analyze β Store
π Sampling
Random = good
Non-random = biased
π§ͺ Experiments
Control vs Experimental
Watch out for bias
π Chi-Square
Compare Observed vs Expected
Big value = reject
π§ ULTIMATE MEMORY HACK (EXAM READY)
π βD-S-S-E-Cβ
Data Management
Sampling
Surveys
Experiments
Chi-square