We thoroughly check each answer to a question to provide you with the most correct answers. Found a mistake? Tell us about it through the REPORT button at the bottom of the page. Ctrl+F (Cmd+F) will help you a lot when searching through such a large set of questions.
One of the assumptions we sometimes need to make when performing statistical inferences is that the response variable in the population has a Normal distribution. Is it possible to check that this assumption is satisfied?
- No – we can’t really check this assumption since all data will look perfectly bell-shaped and symmetric, even if the population was not Normal.
- Yes – we can be absolutely sure the population was Normal if a plot of the data has no major outliers.
- No – we can’t really check this assumption since we don’t have the whole population, but the t distribution is robust to modest departures from Normality, so we can use it if a plot of the data has no major outliers.
- Yes – we can be absolutely sure the population was Normal if a plot of the data looks roughly bell-shaped and symmetric.
- Yes – we can be absolutely sure the population was Normal if a plot of the data looks perfectly bell-shaped and symmetric.
Answer
The correct answer is: No – we can’t really check this assumption since we don’t have the whole population, but the t distribution is robust to modest departures from Normality, so we can use it if a plot of the data has no major outliers.
What is Statistical Inference?
A common assumption made when doing statistical inferences is that the response variable in the population has a normal distribution. This assumption is important because many statistical procedures are based on normal distribution. When the response variable does not have a normal distribution, the results of the statistical procedure may be inaccurate.
What is a Type I Error?
A type I error is when you conclude that there is a difference between two groups when there actually is no difference. For example, you may conclude that treatment A is better than treatment B when in reality they are both equally effective. The probability of making a type I error is denoted by alpha (α).
What is a Type II Error?
A type II error is when you conclude that there is no difference between two groups when in reality there is a difference. For example, you may conclude that treatments A and B are equally effective when in reality, treatment A is better than treatment B. The probability of making a type II error is denoted by beta (β).
What is the Power of a Statistical Test?
The power of a statistical test is the probability of correctly rejecting the null hypothesis when it is false. In other words, it’s the likelihood that you won’t make a type II error. The power of a test is affected by several factors, including:
- The size of the difference between the groups being compared (the larger the difference, the easier it will be to detect)
- The variability of the response variable (the more variable the responses are, the harder it will be to detect a difference)
Was this helpful?
Quizzma Team
The Quizzma Team is a collective of experienced educators, subject matter experts, and content developers dedicated to providing accurate and high-quality educational resources. With a diverse range of expertise across various subjects, the team collaboratively reviews, creates, and publishes content to aid in learning and self-assessment.
Each piece of content undergoes a rigorous review process to ensure accuracy, relevance, and clarity. The Quizzma Team is committed to fostering a conducive learning environment for individuals and continually strives to provide reliable and valuable educational resources on a wide array of topics. Through collaborative effort and a shared passion for education, the Quizzma Team aims to contribute positively to the broader learning community.
