Applying Hypothesis Testing in the Real World
Hypothesis testing allows data scientists to objectively assess whether an observed pattern is likely due to chance or reflects a genuine effect.
Null vs. Alternative Hypotheses
Every test starts with a null hypothesis, representing the status quo, and an alternative hypothesis, representing a potential effect. By choosing a significance level and calculating a p-value, we can decide whether to reject the null hypothesis.
Common Pitfalls
Misinterpreting p-values or failing to consider effect sizes can lead to misguided conclusions. Always pair statistical significance with domain context to ensure results are meaningful.
