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What is another name for the probability of observing a sample value at least as extreme as a given on under a null hypothesis?
Wiki User
∙ 11y ago
Wiki User
∙ 11y ago
Wiki User
∙ 11y agoThe significance level of the observation - under the null hypothesis.
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How do you compute the p-value?
The first step in calculating a p-value is to make a hypothesis of the statistical model for your study. You then assume that the hypothesis is true and calculate the probability of observing an outcome at least as extreme as the one that you did observe. This probability is the p-value.
When do you use a z score table?
If you have a variable whose distribution is approximately Gaussian (Normal), then the z-score gives the probability of observing a value that is equal to or more extreme. This is usually in the context of testing some hypothesis about the mean of the variable.A very low probability would suggest that your hypothesis is wrong or that your assumptions about the data are wrong or that you have just had the misfortune of an unlikely event actually happening!
In a field if statistics what is used to predict or test hypothesis?
A statistical model is fitted to the data. The extent to which the model describes the data can be tested using standard tests - including non-parametric ones. If the model is a good fit then it can be used to make predictions.A hypothesis is tested using a statistic which will be different under the hypothesis being tested and its alternative(s). The procedure is to find the probability distribution of the test statistic under the assumption that the hypothesis being tested is true and then to determine the probability of observing a value at least as extreme as that actually observed.
Is alpha the probability that the test statistic would assume a value as or more extreme than the observed value of the test?
Alpha is the probability that the test statistics would assume a value as or more extreme than the observed value of the test, BY PURE CHANCE, WHEN THE NULL HYPOTHESIS IS TRUE.
What is the difference between a test statistic and a critical value?
A test statistic is a value calculated from a set of observations. A critical value depends on a null hypothesis about the distribution of the variable and the degree of certainty required from the test. Given a null hypothesis it may be possible to calculate the distribution of the test statistic. Then, given an alternative hypothesis, it is may be possible to calculate the probability of the test statistic taking the observed (or more extreme) value under the null hypothesis and the alternative. Finally, you need the degree of certainty required from the test and this will determine the value such that if the test statistic is more extreme than the critical value, it is unlikely that the observations are consistent with the hypothesis so it must be rejected in favour of the alternative hypothesis. It may not always be possible to calculate the distribution function for the variable.
How do you compute the p-value?
The first step in calculating a p-value is to make a hypothesis of the statistical model for your study. You then assume that the hypothesis is true and calculate the probability of observing an outcome at least as extreme as the one that you did observe. This probability is the p-value.
What is Hypothesis Testing of p-value?
The probability of the observed value or something more extreme under the assumption that the null hypothesis is true. That is, the probability of standard scores at least as extreme as the observed test statistic.
When do you use a z score table?
If you have a variable whose distribution is approximately Gaussian (Normal), then the z-score gives the probability of observing a value that is equal to or more extreme. This is usually in the context of testing some hypothesis about the mean of the variable.A very low probability would suggest that your hypothesis is wrong or that your assumptions about the data are wrong or that you have just had the misfortune of an unlikely event actually happening!
If a test of hypothesis has a type I error probability of 0.01 it means?
It means that, if the null hypothesis is true, there is still a 1% chance that the outcome is so extreme that the null hypothesis is rejected.
In a field if statistics what is used to predict or test hypothesis?
A statistical model is fitted to the data. The extent to which the model describes the data can be tested using standard tests - including non-parametric ones. If the model is a good fit then it can be used to make predictions.A hypothesis is tested using a statistic which will be different under the hypothesis being tested and its alternative(s). The procedure is to find the probability distribution of the test statistic under the assumption that the hypothesis being tested is true and then to determine the probability of observing a value at least as extreme as that actually observed.
How is the p-value used?
The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
How is the null hypothesis used in hypothesis testing?
Statistical tests compare the observed (or more extreme) values against what would be expected if the null hypothesis were true. If the probability of the observation is high you would retain the null hypothesis, if the probability is low you reject the null hypothesis. The thresholds for high or low probability are usually set arbitrarily at 5%, 1% etc. Strictly speaking, when rejecting the null hypothesis, you do not accept the alternative hypothesis because it is possible that neither are true and it is the model itself that is wrong.
Is alpha the probability that the test statistic would assume a value as or more extreme than the observed value of the test?
Alpha is the probability that the test statistics would assume a value as or more extreme than the observed value of the test, BY PURE CHANCE, WHEN THE NULL HYPOTHESIS IS TRUE.
What does a p value of 0.66 tell us?
A p-value is the probability of obtaining a test statistic as extreme or more extreme than the one actually obtained if the null hypothesis were true. If this p-value is less than the level of significance (usually set by the experimenter as .05 or .01), we reject the null hypothesis. Otherwise, we retain the null hypothesis. Therefore, a p-value of 0.66 tell us not to reject the null hypothesis.
What is the reason of a null hypothesis being rejected?
W The test statistic is is the critical region or it exceeds the critical level. What this means is that there is a very low probability (less than the critical level) that the test statistics could have attained a value as extreme (or more extreme) if the null hypothesis were true. In simpler terms, if the null hypothesis were true you are very, very unlikely to get such an extreme value for the test statistic. And although it is possible that this happened purely by chance, it is more likely that the null hypothesis was wrong and so you reject it.
What forms the basis of your decision to conclude that a numerical value of a variable you have calculated is significant or not significant at the 0.1 percent or 0.5 level in a regression analysis?
You start of with a null hypothesis according to which the variable has some specified distribution. Some of the parameters of this distribution may need to be estimated using the observed data. Against this hypothesis you will have an alternative hypothesis about the distribution of the variable. You then assume that the null hypothesis is true and calculate the probability that the variable (or a test statistic based on that variable) has the observed numerical value or one that is more extreme. (In deciding what is more extreme you need to know the alternative hypothesis.) If that probability is less than 0.1 % then the result is significant at 0.1% - and so on.
What Interpretation of p value statistics?
p value are used when comparing the likelihood of a stated [null] hypothesis being true against a stated alternative. It is a measure of the probability with which an observation which is at least as extreme as that observed will occur even though the null hypothesis is true.
