Okay, you've learned how to set up your hypotheses: the null hypothesis ( $H_0$ ), representing the default state or no effect, and the alternative hypothesis ( $H_1$ ), representing what you're trying to find evidence for. Now, when you use your sample data to decide between these two, you're essentially making a judgment based on incomplete information (the sample, not the whole population). Naturally, this means you might sometimes make the wrong call. In statistics, there are two specific ways you can be wrong, known as Type I and Type II errors. Understanding these is fundamental to interpreting test results correctly.

Type I Error: The False Alarm

A Type I error occurs when you reject the null hypothesis ( $H_0$ ) when it is actually true. Think of it as a "false positive" or a false alarm.

Probability: The probability of making a Type I error is denoted by the Greek letter alpha, $\alpha$ .
Significance Level: This value, $\alpha$ , is also called the significance level of the test. You, the researcher or analyst, choose this value before conducting the test. Common choices for $\alpha$ are 0.05 (5%) or 0.01 (1%).
Meaning: An $\alpha$ of 0.05 means you are willing to accept a 5% chance of incorrectly rejecting the true null hypothesis. If $H_0$ were really true, and you repeated your experiment many times, you'd expect to wrongly reject it about 5% of the time just due to random sampling variability.

Example: Suppose $H_0$ is "The new website design does not increase the conversion rate" and $H_1$ is "The new design does increase the conversion rate". A Type I error would mean concluding the new design is better (rejecting $H_0$ ) when, in reality, it provides no improvement or is even worse, and the observed difference in your sample was just due to chance. The consequence might be wasting resources implementing an ineffective design.

Type II Error: The Missed Detection

A Type II error occurs when you fail to reject the null hypothesis ( $H_0$ ) when it is actually false (meaning the alternative hypothesis, $H_1$ , is true). This is like a "false negative" or failing to detect an effect that is really there.

Probability: The probability of making a Type II error is denoted by the Greek letter beta, $\beta$ .
Power: The probability of correctly rejecting a false $H_0$ is called the power of the test, and it equals $1 - \beta$ . Power represents the test's ability to detect a real effect.
Factors Influencing $\beta$ : Unlike $\alpha$ , $\beta$ is not usually set directly. It depends on several factors, including the chosen $\alpha$ , the sample size ( $n$ ), the true difference between the hypothesized value and the actual population value (the effect size), and the variability in the data.

Example: Using the same website design scenario ( $H_0$ : no increase, $H_1$ : increase). A Type II error would mean concluding the new design is not better (failing to reject $H_0$ ) when, in fact, it does improve the conversion rate. The consequence here is a missed opportunity to implement a beneficial change.

The Inevitable Trade-off

When conducting a hypothesis test with a fixed sample size, there's an inherent trade-off between $\alpha$ and $\beta$ .

If you make your criterion for rejecting $H_0$ very strict (e.g., choosing a very small $\alpha$ , like 0.001), you reduce the chance of a Type I error. However, this makes it harder to reject $H_0$ overall, thus increasing the chance of failing to detect a real effect (increasing $\beta$ , decreasing power).
Conversely, if you make it easier to reject $H_0$ (e.g., choosing a larger $\alpha$ , like 0.10), you increase the power of the test to detect a true effect (reducing $\beta$ ), but you also increase the risk of a Type I error.

This relationship is visualized below:

Decision outcomes in hypothesis testing, illustrating Type I ( $\alpha$ ) and Type II ( $\beta$ ) errors.

Choosing the significance level $\alpha$ often depends on the relative consequences of making each type of error in a specific context. If a Type I error is very costly (e.g., approving a faulty medical device), you might choose a very small $\alpha$ . If a Type II error is more concerning (e.g., missing a potentially effective treatment), you might accept a slightly higher $\alpha$ to increase the test's power ( $1 - \beta$ ).

In machine learning, this translates directly to model evaluation and feature selection:

Model Comparison: Is model B significantly better than model A? A Type I error means claiming B is better when it's not. A Type II error means failing to recognize B's superiority when it exists.
Feature Importance: Does feature X have a significant impact on the target variable? A Type I error means deeming a useless feature important. A Type II error means overlooking a genuinely useful feature.

Understanding this framework is essential before we proceed to calculate p-values and perform specific tests like t-tests and Chi-squared tests using Python libraries in the upcoming sections. These errors quantify the risks associated with making decisions based on statistical evidence.