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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.
Wiki User
∙ 11y ago
Wiki User
∙ 11y agoIn 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.
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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.
Is a 95 percent confidence interval for a mean wider than a 99 percent confidence interval?
No, it is not. A 99% confidence interval would be wider. Best regards, NS
What does a 95 percent confidence interval tell you about the population proportion?
There is a 95% probability that the true population proportion lies within the confidence interval.
Difference between 95 percent and 99 percent confidence interval?
4.04%
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 is the Z value for 95 percent confidence interval estimation?
1.96
What is meant by a 95 percent confidence interval?
Confidence IntervalsConfidence interval (CI) is a parameter with a degree of confidence. Thus, 95 % CI means parameter with 95 % of confidence level. The most commonly used is 95 % confidence interval.Confidence intervals for means and proportions are calculated as follows:point estimate ± margin of error.
When the sample size and sample standard deviation remain the same a 99 percent confidence interval for a population mean will be narrower than the 95 percent confidence interval for the mean?
Never!
When the sample size increase what will happen to the 95 percent confidence interval?
It becomes narrower.
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.
When determining the 95 percent confidence interval for a population mean with known sigma the value of the critical value of z is equal to?
1.96
When comparing the 95 percent confidence and prediction intervals for a given regression analysis what is the relation between confidence and prediction interval?
Confidence interval considers the entire data series to fix the band width with mean and standard deviation considers the present data where as prediction interval is for independent value and for future values.
What does 95 percent confidence interval mean?
You construct a 95% confidence interval for a parameter such as mean, variance etc. It is an interval in which you are 95 % certain (there is a 95 % probability) that the true unknown parameter lies. The concept of a 95% Confidence Interval (95% CI) is one that is somewhat elusive. This is primarily due to the fact that many students of statistics are simply required to memorize its definition without fully understanding its implications. Here we will try to cover both the definition as well as what the definition actually implies. The definition that students are required to memorize is: If the procedure for computing a 95% confidence interval is used over and over, 95% of the time the interval will contain the true parameter value. Students are then told that this definition does not mean that an interval has a 95% chance of containing the true parameter value. The reason that this is true, is because a 95% confidence interval will either contain the true parameter value of interest or it will not (thus, the probability of containing the true value is either 1 or 0). However, you have a 95% chance of creating one that does. In other words, this is similar to saying, "you have a 50% of getting a heads in a coin toss, however, once you toss the coin, you either have a head or a tail". Thus, you have a 95% chance of creating a 95% CI for a parameter that contains the true value. However, once you've done it, your CI either covers the parameter or it doesn't.
