Statistics MCQs, Data Analysis, Stat Software
Level of significance, Type I and Type II Errors, Level of Risk, Power of Test, False Positive, False Negative, Rejection or Acceptance of Hypothesis
The post covers the Type I and Type II Errors examples. Hypothesis testing helps us to determine whether the results are statistically significant or occurred by chance. Hypothesis testing is based on probability, therefore, there is always a chance of making the wrong decision about the null hypothesis (a hypothesis …
Difference Between the P value and Significance Level? Basically in hypothesis testing the goal is to see if the probability value is less than or equal to the significance level (i.e., is p ≤ alpha). It is also called the size of the test or the size of the critical …
Type I and Type II Errors In hypothesis testing, there are two possible errors we can make: Type I and Type II errors. If you do reject your null hypothesis, then it is also essential that you determine whether the size of the relationship is practically significant.The hypothesis test procedure …
Significance Level The significance level in statistics is the level of probability at which it is agreed that the null hypothesis will be rejected. In academic research, usually, a 0.05 level of significance (level of significance) is used. The level of significance is also called a level of risk. Significance …