Common confidence levels: what they mean for experiments

Ever run an experiment and wondered just how sure you can be about the results? Confidence levels might sound like geeky statistical jargon, but they're actually pretty straightforward—they tell us how reliable our findings are. Whether you're a seasoned researcher or just dipping your toes into data analysis, understanding confidence levels is key to making informed decisions.

In this blog, we'll break down what confidence levels are, why that magic number 95% keeps popping up, and how to avoid common pitfalls when interpreting your data. Plus, we'll chat about when and why you might adjust your confidence levels. Let's dive in!

Understanding confidence levels in experiments

So, what's the deal with confidence levels in experiments? They're all about measuring how certain we are that a parameter falls within a certain range—what we call a confidence interval. Basically, confidence levels help us figure out just how reliable our experimental results are.

You might have heard of the 95% confidence level—it's pretty standard in research. This means that if we ran the experiment 100 times, we'd expect the true parameter to fall within our confidence interval in 95 of those trials. But here's the catch: it doesn't mean there's a 95% chance the true parameter is in any single interval we calculate.

Confidence levels help us make informed decisions by quantifying the precision and certainty of our estimates. If we crank up the confidence level to 99%, we're more certain, but our confidence intervals get wider and less precise. On the flip side, lowering it to 90% gives us narrower intervals—more precision but less certainty.

Choosing the right confidence level really depends on what you're trying to achieve. Sometimes, you might need a higher confidence level to avoid false positives. Other times, a lower level might be fine if you're more worried about false negatives. Understanding how different confidence levels impact your results is key to designing effective experiments and making sense of the data.

The 95% confidence interval: why it's the go-to choice

Ever wonder why the 95% confidence interval is so popular? We can thank Sir Ronald Fisher for that. It's become the gold standard in statistics because it strikes a good balance between being practical and rigorous. Plus, since it corresponds to about two standard deviations, it makes calculations and interpretations a bit easier.

Think of the 95% confidence interval as a gatekeeper in hypothesis testing. It helps ensure that only statistically significant results get through, cutting down on false positives. By using a 5% significance level to reject the null hypothesis, we've got a widely accepted threshold that most researchers agree on.

But let's not forget—sometimes it makes sense to adjust this threshold. For instance:

• Startups with a high risk tolerance might go for lower confidence levels to avoid missing out on potential positives (minimizing false negatives).

• Critical systems, like in healthcare, might need higher confidence levels to prevent false alarms (reducing false positives).

Even though some people argue that the 95% level is either too strict or too lenient, it's still a staple in scientific research and online testing—including at Statsig. It aligns nicely with the p-value approach and gives us insight into the precision of our estimates—a narrower interval means we're more precise. Grasping how confidence levels work helps us make smarter, data-driven decisions.

Misinterpretations and common pitfalls with confidence levels

Confidence levels can be tricky, and it's easy to misunderstand what they really mean. A common mistake is thinking that a 95% confidence level means there's a 95% chance the true parameter is inside your particular interval. But that's not quite right. What it actually means is if we repeated the experiment many times, 95% of those confidence intervals would contain the true parameter.

These misunderstandings can lead to wrong conclusions and mess up your experimental outcomes. For example, you might see a narrow confidence interval and think you've got high precision—but if your sample size is tiny or your data is biased, that's not necessarily true. On the flip side, a wide interval might seem unhelpful, but it actually tells us about the uncertainty around our estimate.

So how do we avoid these traps? It's important to grasp how confidence levels relate to things like sample size, variability, and confounding factors. When you're reporting results, make sure to include both the p-value and the confidence interval. This gives a fuller picture of your findings. At Statsig, we emphasize understanding these concepts so you can make smarter, data-driven decisions.

Adjusting confidence levels: when and why

Here's the thing—confidence levels aren't a one-size-fits-all deal; you can tweak them depending on what your experiment needs. Higher confidence levels, like 99%, give you more certainty that the true parameter is within your interval but at the cost of wider, less precise intervals. Lower confidence levels, say 90%, make your intervals narrower (more precise) but with less certainty.

So how do you pick the right confidence level? It comes down to context and your risk tolerance. In areas like medical research, where a false positive could be dangerous, higher confidence levels are the way to go. But in online experimentation—like what we often do at Statsig—where the stakes are lower, you might be okay with a lower confidence level to move faster.

It's all about balancing precision and certainty. While the 95% confidence level is a common middle ground, adjusting it to fit your specific experiment can make sense. Just remember to set your confidence level before you start collecting data, so your study stays aligned with its goals and risks.

Closing thoughts

Understanding confidence levels is crucial for interpreting experimental results and making informed decisions. By grasping how they work, you can design better experiments, avoid common pitfalls, and adjust your approach based on your specific needs. Whether you stick with the classic 95% confidence level or tweak it to suit your situation, the key is to balance precision and certainty in a way that fits your goals.

If you're interested in diving deeper, check out our perspective on interpreting p-values and confidence intervals or explore more about confidence levels in this article.

Hope you found this helpful!