0
In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.
If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.
If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.
If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.
If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.
If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.
If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.
If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.
If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
Still curious? Ask our experts.
Chat with our AI personalities
Jordan
Beau
DevinIn statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.
If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.
If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
Add your answer:
Earn +20 pts
What would happen to the width of the confidence interval if the level of confidence is lowered from 95 percent to 90 percent?
decrease
What does it mean to have 95 percent confidence in an interval estimate?
It means that 95% of the values in the data set falls within 2 standard deviations of the mean value.
How is the term confidence interval defined?
A term used in inferential statistics which measures the probability that a population parameter will fall between two set values. The confidence can take any number of probabilities, with most common probabilities being : 95% or 99%.
What does alpha-1 mean in statistics?
Im in statistics class and I been trying to figure out the answer too. I couldn't find it online. Luckily I had the answer to the problem I was trying answer, so to figure out what it was I worked it backwards. BASICALLY I THINK ITS ANOTHER WAY OF SAYING (confidence interval) which for most problems is either 90% 95% 99% or in decimal form is .9 .95 .99
What is Confidence Intervals of Critical Statistic?
Confidence intervals of critical statistics provide a range of values within which we can reasonably estimate the true value of a population parameter based on our sample data. They are constructed by calculating the critical statistic, such as the mean or proportion, and then determining the upper and lower bounds of the interval using the standard error and a desired level of confidence, usually 95% or 99%. The confidence interval helps us understand the uncertainty around our estimates and provides a measure of the precision of our results.
