DATA MANAGEMENT & STATISTICS

πŸ“Š DATA MANAGEMENT & STATISTICS REVIEWER

(Based on your handouts )


🧠 1. What is Data & Data Management?

πŸ”Ή What is Data?
  • Data = raw facts or information

  • Can be:

    • Qualitative β†’ descriptions (e.g., color, opinions)

    • Quantitative β†’ numbers (e.g., age, height)

πŸ‘‰ Example:

  • β€œBlue” = qualitative

  • β€œ18 years old” = quantitative


πŸ”Ή What is Data Management?

Think of this as alaga system ng data mo.

  • Organizing data

  • Checking errors

  • Preparing for analysis

  • Storing & documenting data

πŸ’‘ In short, ganito lang β€˜yan:

Data management = β€œfrom gulo β†’ organized β†’ useful info”


πŸ”₯ Importance (Why it matters)
  • Ensures accurate conclusions

  • Makes data reusable (future studies)

  • Improves efficiency & quality


🧠 Quick Memory Trick:

πŸ‘‰ D-A-T-A

  • Data organized

  • Analysis-ready

  • Trustworthy

  • Archived


πŸ“Š 2. Methods of Data Collection

πŸ”Ή 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


πŸ’‘ Key Insight:
  • Census = complete but expensive

  • Sampling = cheaper & faster

πŸ‘‰ Real-life:

Shopee reviews? That’s sampling, not census πŸ˜†


🧠 Memory Trick:

πŸ‘‰ CS-E-O

  • Census

  • Sample

  • Experiment

  • Observation


🎯 3. Surveys & Sampling

πŸ”Ή Good Survey = Dapat ganito:

  • Representative (fair sample)

  • Random selection

  • Neutral questions

  • Controlled bias

πŸ’‘ Example:
Bad question ❌: β€œDo you agree this product is amazing?”
Good question βœ…: β€œHow would you rate this product?”


πŸ”Ή Sampling Methods

πŸ”΄ Non-Probability Sampling
  • NOT random

  • Biased

Examples:

  • Convenience sampling

  • Quota sampling

πŸ‘‰ Example:

Interview mo lang mga friends mo = biased agad 😬


🟒 Probability Sampling (Better πŸ”₯)
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)


🧠 Memory Trick:

πŸ‘‰ β€œSi Syempre Study Classes Mo”

  • Simple

  • Systematic

  • Stratified

  • Cluster

  • Matched


πŸ§ͺ 4. Experiments (Super Important πŸ”₯)

πŸ”Ή Key Components

  • Control Group β†’ walang treatment

  • Experimental Group β†’ may treatment

  • Random Assignment β†’ fair distribution

  • Replication β†’ repeat for accuracy


πŸ’‘ Real-life Example:
  • Testing new skincare:

    • Group A: gumagamit

    • Group B: hindi


⚠ Bias & Errors

πŸ”Έ Confounding Variable
  • Hidden factor affecting results

πŸ‘‰ Example:
Ice cream sales ↑ and drowning ↑
➑ Real cause: summer (confounder)


πŸ”Έ Placebo Effect
  • Fake treatment but may effect pa rin

πŸ”Έ Blinding
  • Participants don’t know treatment


πŸ”Έ Blocking
  • Group similar subjects

πŸ‘‰ Example:
Separate males & females in study


🧠 Memory Trick:

πŸ‘‰ CPBB

  • Confounding

  • Placebo

  • Blinding

  • Blocking


πŸ§ͺ 5. Experimental Designs

Design

Meaning

Completely Randomized

Pure random

Randomized Block

Group then random

Matched Pairs

Pair subjects


πŸ’‘ In short:

  • Random = simple

  • Block = organized

  • Matched = paired


πŸ“ˆ 6. Chi-Square Test (Very Exam Favorite πŸ”₯)

πŸ”Ή Purpose:

To check if:

  • Data matches expectation OR

  • Two variables are related


πŸ”Ή Formula

Ο‡2=βˆ‘(Oβˆ’E)2E\chi^2 = \sum \frac{(O - E)^2}{E}Ο‡2=βˆ‘E(Oβˆ’E)2​

Where:

  • O = Observed

  • E = Expected


πŸ”Ή Types of Chi-Square

1. Goodness of Fit

πŸ‘‰ Checks if data matches expected distribution

2. Test of Independence

πŸ‘‰ Checks if 2 variables are related


🧠 Key Idea:
  • Small χ² β†’ good fit (may relationship)

  • Large χ² β†’ poor fit (no match)


πŸ”Ή Assumptions

  • Random sample

  • Independent observations

  • Expected β‰₯ 5


πŸ”Ή Hypothesis

Test

Hβ‚€

H₁

Goodness

Matches expected

Not match

Independence

No relationship

Has relationship


πŸ”₯ Example Insight (from your handout)

πŸ‘‰ On page 6–7,

  • If χ² > critical value β†’ Reject Hβ‚€

  • Meaning: may difference or relationship


🧠 Memory Trick:

πŸ‘‰ β€œO-E squared over E”
(Just memorize flow ng formula)


πŸ“Š 7. Chi-Square Table (Handout 2)

  • Used to find critical value

  • Based on:

    • Degrees of freedom (df)

    • Significance level (Ξ±)

πŸ‘‰ Makikita ito sa table (page 1 of Handout 2)

πŸ’‘ Example:

  • df = 3, Ξ± = 0.05 β†’ critical = 7.815


🧠 Quick Tip:

πŸ‘‰ If computed χ² > table value β†’ Reject Hβ‚€


⚑ FINAL RECAP (Ultra Simplified)

πŸ‘‰ If cram mode ka na, eto na lang tandaan mo:

🧠 Data Management
  • Organize β†’ Clean β†’ Analyze β†’ Store

πŸ“Š Sampling
  • Random = good

  • Non-random = biased

πŸ§ͺ Experiments
  • Control vs Experimental

  • Watch out for bias

πŸ“ˆ Chi-Square
  • Compare Observed vs Expected

  • Big value = reject


🧠 ULTIMATE MEMORY HACK (EXAM READY)

πŸ‘‰ β€œD-S-S-E-C”

  • Data Management

  • Sampling

  • Surveys

  • Experiments

  • Chi-square