Final answer:
Option a is the correct option. The typical alpha level for null hypothesis significance testing is 0.05. This level indicates a 5% risk of making a Type I error, which is rejecting the null hypothesis when it is actually true. Most examples provided use an alpha level of 0.05 to determine whether to reject or not reject the null hypothesis.
Step-by-step explanation:
The question you're asking relates to the typical alpha level used when performing null hypothesis significance testing. The alpha level, also known as the level of significance, represents the probability of making a Type I error, which is rejecting the null hypothesis when it is actually true. In the context of hypothesis testing, this alpha level is commonly set at different thresholds depending on the standards of the field and the specific research context.
The options provided suggest different alpha levels, but the most commonly used alpha level in many fields is 0.05. This means that there is a 5% risk of concluding that a difference exists when there is no actual difference. An alpha level of 0.01 is more conservative, implying a 1% risk, while an alpha level of 0.10 is less conservative, with a 10% risk. Based on the information provided, and commonly accepted standards in statistical testing, an alpha of 0.05 is typically used in null hypothesis significance testing unless stated otherwise.
From the examples given, it seems that an alpha level of 0.05 is frequently used to make decisions about whether to reject or do not reject the null hypothesis. When the p-value is less than the alpha level, the decision is typically to reject the null hypothesis, as it suggests that the observed data is sufficiently inconsistent with the null hypothesis.