HT: Feb 17

1. Love–Hate Relationship with Statistics

  • Field is highly number-dependent

  • BUT statistics are full of complications and limitations

  • Don’t obsess over exact numbers

  • Focus on:

    • Patterns

    • Trends

    • Context

    • Broader influences

Take statistics with a “grain of salt.”

2. External Variables That Influence Trafficking

Human trafficking is affected by factors outside direct trafficking studies, such as:

  • Economic growth or recession

  • Poverty rates

  • Border policies

  • Immigration laws

  • Political instability

  • Changes in criminal law

  • Global labor demand

These variables shape trafficking trends indirectly.

3. “Lies, Damn Lies, and Statistics”

  • Phrase often attributed to Mark Twain

  • Highlights how statistics can be:

    • Manipulated

    • Misinterpreted

    • Politically framed

Key Point:

The data itself is often collected properly —
but how it is presented or used can be political.

4. Politics vs. Data

Important Distinction:

  • Statistical agencies collect data through systematic methods.

  • Politicians may selectively highlight certain numbers.

Example:

  • Labor statistics may be reported neutrally.

  • Political figures may emphasize only the data that supports their narrative.

Lesson:

  • Question who is presenting the data.

  • Consider possible political motivations.

5. Industry Influence on Research

Some industries fund research to protect their interests:

  • Tobacco industry historically funded research to deny health risks.

  • Petroleum industry funds research related to climate debates.

⚠ Be cautious when research is funded by groups with financial interests.

6. Types of Human Trafficking Research

A. Quantitative Research

  • Surveys

  • Government data

  • Statistical projections

  • Large datasets

B. Qualitative Research

  • Survivor interviews

  • Case studies

  • Fieldwork

  • Personal narratives

C. Content Analysis

  • Government reports

  • Media portrayals

  • News articles

  • Documentaries

  • Policy documents

D. Statistical Projections

  • Predicting increases/decreases

  • Modeling impact of political change

7. The “Wiesel Effect” (Repetition Creates Belief)

  • When a claim is repeated frequently

  • Even without solid evidence

  • It begins to feel true

Modern issue:

  • Social media spreads unverified claims quickly

  • Repetition ≠ truth

Always verify sources.

8. Why Good Data Matters

Good data helps us:

  1. Understand the magnitude of a problem

  2. Assess urgency

  3. Identify trends

  4. Prioritize interventions

  5. Develop solutions

Without reliable data → weak or ineffective solutions.

9. Critical Questions to Ask About Statistics

When you see a statistic, ask:

  • What is the definition being used?

  • What time period does this cover?

  • What is the baseline comparison?

  • What is the sample size?

  • Who collected the data?

  • Is it peer-reviewed?

  • Are there limitations disclosed?

10. Examples of Critical Thinking with Statistics

Example 1:

“27–30 million people are enslaved globally.”

Ask:

  • What counts as “slave”?

  • What definition is used?

  • What data sources were used?

Example 2:

“One in six runaways in 2014 was sex trafficked.”

Ask:

  • Age range?

  • How is “runaway” defined?

  • Was this region-specific?

  • Was it verified?

Example 3:

“10% of kidneys transplanted worldwide were illegally obtained.”

Ask:

  • How was this measured?

  • What countries?

  • What year?

  • How reliable are the reporting systems?

11. Sampling & Bias

What is a Sample?

A portion of a population used to estimate trends.

Sampling Problems:

  • Too small

  • Too narrow

  • Only surveying one group

  • Only collecting data at certain times

  • Extreme responders (very satisfied or very dissatisfied)

Poor sampling → distorted conclusions.

12. Survey Limitations

Issues include:

  • Non-response bias

  • Extreme opinion bias

  • Poorly designed demographic questions

  • Lack of anonymity

  • Incomplete disclosure of limitations

13. Critical Issues in Human Trafficking Data

Human trafficking data is especially difficult because:

  • It is hidden activity

  • Victims may not report

  • Definitions vary

  • Smuggling and trafficking get mixed

  • Some countries have weak data systems

  • Some data is outdated

  • Numbers may be politically sensitive

14. Peer Review (Why It Matters)

Peer-reviewed research:

  • Evaluated by experts in the field

  • Checked for methodology issues

  • Reviewed for logical consistency

  • More scientifically reliable

Not necessarily exciting — but more credible.

15. Common Data Problems

  • Outdated statistics

  • Misattributed sources

  • Data taken out of context

  • Weak verification

  • Poor methodology

  • Mixing definitions

  • Staff errors (“garbage in, garbage out”)

  • Large percentage increases without baseline explanation

16. Percentage vs Absolute Numbers

Always check:

  • What was the starting number?

  • Is this growth meaningful?

Example:
“Cases doubled”
But from 2 to 4 → very different than 5,000 to 10,000.

17. Key Takeaways for Human Trafficking Studies

  • Trafficking data is complex and imperfect.

  • Definitions matter.

  • Context matters.

  • Politics influences presentation.

  • Patterns matter more than single numbers.

  • Always ask follow-up questions.

  • Look for transparency in methods.

  • Consider who benefits from the narrative.