Statistical Significance vs Meaningful Significance - Data Demystified Episode

39 flashcards covering statistical significance, meaningful significance, effect sizes, and a practical framework for evaluating results.

What is statistical significance?

It tells us whether a result is unlikely to be due to chance, given enough data and confidence.

What is meaningful significance?

The practical importance or magnitude of the effect—whether it matters in the real world.

What two questions should you ask to evaluate a statistical result?

Is it statistically significant? If yes, is it meaningful/practically significant?

Fill in the blank: Statistical significance tells us if a result is unlikely to be due to .

chance

Fill in the blank: Meaningful significance concerns the of the effect.

magnitude

What does an effect size measure?

How large or impactful the effect is, not just whether it exists.

Why are effect sizes used in medicine or economics?

They quantify how much the intervention changes outcomes and guide trade-offs.

In the vaccine example, what two magnitudes are compared?

10% reduction vs 20% reduction in infection rates.

Can a 1% reduction be statistically significant?

Yes, with enough data; it can be statistically significant even if the practical impact is small.

In economics, besides significance, what else must we know?

The amount of the increase (the magnitude) to assess whether it is worth the cost.

In the free college tuition example, if wages increase by $100 per year, what question arises?

Whether that small increase is worth the cost of implementation.

In the free college tuition example, if wages increase by $50,000 per year, what might this imply?

It could be worth the investment, depending on costs and benefits.

What role do costs play in evaluating significance?

They help determine whether a statistically significant result is economically or practically worthwhile.

What were the observed averages in the uncommon core math example?

Status quo around 80%, uncommon core around 81%.

Why might a 1% improvement not be worth retraining teachers?

Because the effect size is small relative to retraining costs.

What is the main takeaway about statistical significance vs meaningful significance?

Statistical significance asks if something works; meaningful significance asks if it matters.

What is the purpose of the two-question framework?

To critically assess results for both truth (significance) and importance (meaningfulness).

What does 'necessary but not sufficient' mean in this context?

Statistical significance is necessary to rule out chance, but not sufficient to prove meaningfulness.

What is a risk of focusing only on p-values?

You may miss practical tradeoffs and real-world impact.

How does effect size help when comparing interventions (e.g., 10% vs 20%)?

It shows which is more impactful, regardless of statistical significance.

In the vaccine example, can a small but significant reduction justify investment?

It depends on costs, scale, and other factors.

How should policy decisions use effect size?

Weigh costs, benefits, and the magnitude of the effect.

What are common language effect sizes?

A proposed approach to describe effect sizes in more intuitive terms (mentioned as a topic for learning).

How does statistical significance relate to differences between two groups?

It tests whether observed differences are unlikely due to chance.

Give a policy decision example that illustrates meaningfulness.

Providing free college tuition and weighing wage gains against implementation costs.

What does the video promise viewers at the end?

An intuitive framework for judging whether results are meaningful, not just true.

What should you do when evaluating any statistical result rooted in analysis?

Ask if it is statistically significant and whether it is meaningful, considering tradeoffs.

What happens if you ignore meaningful significance?

You may adopt policies with little real benefit relative to cost.

What is a pitfall of focusing on p-values in practice?

You may miss practical significance and cost-benefit trade-offs.

What is the purpose of significance testing?

To determine whether observed differences are likely not due to random chance.

What is the uncommon core example used to illustrate?

A small but statistically significant improvement that may not be practically meaningful.

What does the trade-off decision depend on?

The size of the effect and the costs/benefits of implementing.

In advertising, what does 'outperform on a key metric' mean?

One advertisement achieves a better metric like favorability, with statistical significance.

Fill in the blank: Meaningful significance focuses on the of the result.

practical importance

What is a vaccine decision cost-benefit example?

Weighing distribution/manufacturing costs against the infection risk reduction.

What is the central purpose of the two-question framework?

To decide whether results should be implemented given practical tradeoffs.

What does 'data rich world' imply for significance analysis?

We have more data; significance testing is common and can detect small effects.

What action is encouraged for viewers to engage with the content?

Like the video, subscribe to the channel, and click the bell for new content.

Why is meaningful significance important for real-world decision making?

To avoid acting on statistically significant but trivially important results and focus on value.