Merkitsevyystasot

Merkitsevyystasot, often translated as significance levels, are a fundamental concept in statistical hypothesis testing. They represent the probability of rejecting a true null hypothesis, a type of error known as a Type I error. When conducting a statistical test, a predetermined significance level, commonly denoted by alpha ($\alpha$), is chosen. This value serves as a threshold for determining whether the observed results are statistically significant.

The most commonly used significance level is 0.05, or 5%. This means that there is a 5% chance of concluding that a difference or relationship exists when, in reality, none does. Other common levels include 0.01 (1%) and 0.10 (10%), with the choice depending on the field of study and the acceptable risk of making a Type I error.

If the p-value obtained from a statistical test is less than the chosen significance level ($\text{p-value} < \alpha$), the null hypothesis is rejected. This suggests that the observed data is unlikely to have occurred by random chance alone, and therefore, the alternative hypothesis is supported. Conversely, if the p-value is greater than or equal to the significance level ($\text{p-value} \ge \alpha$), the null hypothesis is not rejected. This does not necessarily mean the null hypothesis is true, but rather that there is insufficient evidence from the data to reject it at the chosen significance level. Choosing an appropriate significance level is crucial for interpreting the results of hypothesis tests correctly and avoiding misleading conclusions.