What is significance level?

Significance Level, denoted as alpha (α), is a key concept in hypothesis testing. It serves as the threshold to decide if your test results are statistically significant.

Common significance levels are 0.01 and 0.05. These values correspond to a 1% and 5% chance of rejecting the null hypothesis when it’s actually true. This helps you understand the probability of making a Type I error.

Why use a significance level?

Setting a significance level allows you to control the likelihood of incorrectly rejecting a true null hypothesis. This makes your results more reliable.

• 0.05: Indicates a 5% risk of concluding a difference exists when there isn't one.

• 0.01: Indicates a 1% risk, making it more stringent.

How to choose a significance level?

Choosing the right significance level depends on the context of your test. For most experiments, 0.05 is common. However, in more critical fields like medicine, 0.01 might be preferred to minimize errors.

Why significance level matters

Setting a significance level helps you control Type I errors. It prevents you from incorrectly rejecting a true null hypothesis. This makes your experiment results reliable.

Reliability: A clear significance level ensures your findings aren't due to random chance. For more details, check out Statistical Significance.

Trustworthiness: Results become trustworthy, allowing for confident decision-making. By minimizing errors, you enhance the credibility of your study. This is crucial for drawing meaningful conclusions and making informed choices. Learn more about Type 1 Error and Type 2 Error.

Example: medical drug effectiveness

• Scenario: Testing a new drug against a placebo.

• Null Hypothesis (H0): The new drug has no effect.

• Alternative Hypothesis (H1): The new drug is effective.

• Significance Level: Set at 0.01.

• Interpretation: If p-value ≤ 0.01, reject H0, indicating the drug is effective.

You test a new drug against a placebo to see if it works. The null hypothesis states the drug has no effect. The alternative hypothesis claims the drug is effective.

A significance level of 0.01 means there's a 1% risk of a false positive. Collect your data and calculate the p-value. If this p-value is ≤ 0.01, you reject the null hypothesis.

This result means the drug likely has a real effect. You can then consider the drug effective based on your data. This ensures your conclusions are reliable.

For more information on hypothesis testing and interpretation of results, you can refer to Statistical Significance and Sequential Testing.

Example: marketing campaign analysis

• Scenario: Evaluating the impact of a new marketing campaign on sales.

• Null Hypothesis (H0): The campaign has no effect on sales.

• Alternative Hypothesis (H1): The campaign increases sales.

• Significance Level: Set at 0.05.

• Interpretation: If p-value ≤ 0.05, reject H0, indicating the campaign is effective.

You want to know if a new marketing campaign boosts sales. The null hypothesis suggests there's no sales impact. The alternative hypothesis suggests the campaign increases sales.

Set your significance level at 0.05. This means a 5% chance of wrongly rejecting the null hypothesis. Collect your sales data and calculate the p-value.

If the p-value is ≤ 0.05, reject the null hypothesis. This indicates the campaign likely increased sales. Your decision will be backed by data, ensuring reliability.