When I interview candidates for R roles, I find that many can write a complex tidyverse pipeline in their sleep, but they freeze up the moment I ask them to interpret the results of a linear model. There is a massive gap between "knowing the function" and "knowing the statistics." The most common trap you'll walk into during an interview is the over-reliance on the p-value. It's the "magic number" that many beginners treat as a binary switch for truth.

The P-Value obsession

Imagine an interviewer asks you to analyze a dataset measuring the impact of a new UI feature on user session length. You run a simple linear regression: model <- lm(session_length ~ feature_enabled, data = df). You call summary(model), see a p-value of 0.002, and confidently tell the interviewer, "The feature significantly increases session length."

In a junior interview, you might get away with that. In a senior interview, that's a red flag. Why? Because you've fallen for the naive interpretation that statistical significance equals practical significance. You've told them the effect is likely not zero, but you haven't told them if the effect actually matters. I've seen cases where a p-value is 0.0001, but the actual increase in session length is 0.5 seconds. If you're spending $50k a month on server costs to support that feature, a half-second gain is a failure, regardless of how "significant" the p-value is.

Focusing on Effect Size and Confidence Intervals

The better way to handle this—and the way that makes you look like an experienced engineer—is to pivot the conversation toward effect size and confidence intervals. Instead of staring at the Pr(>|t|) column, look at the Estimate. If the coefficient is 0.5, ask yourself: "Does a 0.5 unit increase solve the business problem?"

I always recommend using confint(model) to see the range of the effect. A p-value is a point estimate of probability; a confidence interval gives you a range of plausible values. If your confidence interval for the increase in session length is [0.1, 0.9], you can see that even in the best-case scenario, the gain is marginal. By discussing the interval, you're demonstrating that you understand the uncertainty inherent in the data, rather than treating a p-value like a "Yes/No" button.

Where the logic breaks with large N

Here is the nuance that usually separates the pros from the amateurs: the relationship between sample size and p-values. In R, if you have a dataset with a million rows, almost everything becomes statistically significant. You can find a p-value of 0.01 for the correlation between a user's favorite color and their session length simply because the sample size is so massive that the standard error shrinks to nearly zero.

If you rely on the naive approach, you'll end up reporting a dozen "significant" findings that are actually noise. When you're in the hot seat, explain that as N increases, the power of the test increases, making the p-value a less reliable proxy for importance. The trade-off here is between precision and relevance. High precision (a tiny p-value) doesn't guarantee relevance (a large effect). I've learned the hard way that the most dangerous thing you can do in a data role is mistake a large sample size for a strong signal.

📋 Practical Task

Distinguishing Practical vs. Statistical Significance

You are provided with a simulated dataset representing two different website layouts (A and B) and the time users spent on the page in seconds. Your goal is to prove that relying solely on p-values can lead to poor business decisions.

# Setup code
set.seed(42)
n <- 10000 # Large sample size
layout_a <- rnorm(n, mean = 30, sd = 5)
layout_b <- rnorm(n, mean = 30.2, sd = 5) # Only a 0.2 second difference
df <- data.frame(
  time = c(layout_a, layout_b),
  layout = factor(rep(c("A", "B"), each = n))
)

# Your task:
# 1. Run a linear model (lm) to see if layout affects time.
# 2. Extract the p-value and the coefficient (estimate).
# 3. Calculate the 95% confidence interval for the difference.
# 4. Write a brief comment in your code explaining why, despite the 
#    p-value, you would recommend AGAINST switching to layout B.