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Which table do you have to look at to find the level of significance- i.e alpha equals .05 when testing a hypothesis?
Wiki User
∙ 10y ago
The answer depends on what the underlying probability distribution function is.
Most statistical textbooks will contain tables for the binomial, Poisson, Standard Normal, Student's t, Fisher's F, Chi-squared, Pearson's r tests. Then there are lots of non-parametric tests (Spearman's Rank, Mann Whitney U, Kolmogorov-Smirnoff for example) for which there are other tables but these are less widely published. And lastly, there are tests for even more esoteric distributions.
Wiki User
∙ 10y ago
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What is the Probabilities of Alpha Value?
The risk you are willing to take probabilistically speaking. In general, confidence plus risk is 100%; either your confident or you are taking a risk. In hypothesis testing, it is the probability of rejecting a true null hypothesis.
What is an alpha error?
An alpha error is another name in statistics for a type I error, rejecting the null hypothesis when the null hypothesis is true.
If Relationship between statistical significance and rejectingaccepting an hypothesis?
I believe you asked for the relationship between "statistical significance" and hypothesis testing. In hypothesis testing, we state the null and alternative hypothesis, then in the traditional method, we use a test statistic and a significance level, alpha, to decide whether to accept or reject the null hypothesis in favor of the alternative. If our test statistic falls in the reject area (critical region) of the sampling distribution, then we reject the null hypothesis. If not, we accept it. There is the second method, the p-value method, which is similar in that an alpha value has to be selected. Now, the term "statistical significant result", as used in statistics, means a result (mean value, proportion or variance) from a random sample was not likely to be produced by chance. When we reject the null hypothesis in favor of the alternative, we indicate our data supports an alternative hypothesis, so our result is "statistically significant." Let me use an example. Generally workers arrive at work a few minutes more or less than required. Our null hypothesis will be an average lateness of 5 minutes, and our alternative hypothesis will be greater than 5 minutes. Our data shows an average lateness of 12 minutes, and our test statistic, taking into account the variance and sample size, and our chosen alpha level, concludes that we reject the null hypothesis, so the 12 minute average is a significantly significant result because it supported rejection of the hypothesis. The problem is that significant, in common usage, means important or meaningful, not trivial or spurious. The sample used to calculate late time may have been not randomly chosen, more people come to work late in bad weather. The sample is to make inferences on the a general population, but there is no static population in this case, as a company hires and fires employees. So, since our data is flawed, so can our conclusions. Used as a technical term in statistics, statistical significance has a much more rigorous and restricted meaning, which can lead to confusion. See: http://en.wikipedia.org/wiki/Statistical_significance
Would I accept or reject the null hypothesis if the probability of the obtained statistic is 0.001 and alpha is 0.05?
You should reject the null hypothesis.
What does z equal for an a equals 0.01 and a lower level test?
For a lower level test with significance level (alpha) 0.01, the z value is -2.33. That is, P( z < -2.33) = 0.01. The area to the left of -2.33 is 0.01.
What is Hypothesis Testing of Alpha Value?
Probability of rejecting a true null hypothesis; that is, the alpha value or risk you are willing to take probabilistically speaking.
What is statistical test of hypothesis?
A statistical hypothesis test will usually be performed by inductively comparing results of experiments or observations. The number or amount of comparisons will generally dictate the statistical test to use. The researcher is basically making a statement and assuming that it is either correct (the hypothesis - H1) or assuming that it is incorrect (the null hypothesis - H0) and testing that assumption within a predetermined significance level - the alpha.
Which is better alpha testing or beta testing?
alpha. it's a more private testing.
.01 criterion of significance what is the percent type you error?
I believe you are asking about hypothesis testing, where we choose an alpha value, (also called a signifance level). Thus, I will rephrase your question as follows: If I choose an alpha value of 0.01, what percent of time do you expect the come to an erroneous conclusion, that is test statistic to fall out of the critical region yet the null hypothesis is true? The answer is 1% of the time, an incorrect rejection of the null hypotheis, which is a type I error.
If we reject the null hypothesis what can we conclude about the alpha risk?
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
What is the Probabilities of Alpha Value?
The risk you are willing to take probabilistically speaking. In general, confidence plus risk is 100%; either your confident or you are taking a risk. In hypothesis testing, it is the probability of rejecting a true null hypothesis.
What happens if a hypothesis is tested and shown to be false?
We do not make a clear separation between "proven true" and "proven false" in hypothesis testing. Hypothesis testing in statistical analysis is used to help to make conclusions based on collected data. We always have two hypothesis and must chose between them. The first step is to decide on the null and alternative hypothesis. We also must provide an alpha value, also called a level of significance. Our null hypothesis, or status quo hypothesis is what we might conclude without any data. For example, we believe that Coke and Pepsi tastes the same. Then we do a survey, and many more people prefer Pepsi. So our alternative hypothesis is people prefer Pepsi over Coke. But our sample size is very small, so we are concerned about being wrong. From our data and level of significance, we find that we can not reject the null hypothesis, so we must conclude that Coke and Pepsi taste the same. The options in hypothesis testing are: Null hypothesis rejected, so we accept the alternative or Null hypothesis not rejected, so we accept the null hypothesis. In the taste test, we could always do a larger survey to see if the results change. Please see related links.
How do you perform a Statistical Hypothesis Testing?
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null hypothesis.
What is an alpha error?
An alpha error is another name in statistics for a type I error, rejecting the null hypothesis when the null hypothesis is true.
How do you get alpha pirate in aqw?
It was in alpha testing :l
Which kind of user invoves in alpha testing?
Alpha testing is always performed by the developers at the software development site.
What is alpha in regression analysis?
Alpha is not generally used in regression analysis. Alpha in statistics is the significance level. If you use a TI 83/84 calculator, an "a" will be used for constants, but do not confuse a for alpha. Some may, in derivation formulas for regression, use alpha as a variable so that is the only item I can think of where alpha could be used in regression analysis. Added: Though not generally relevant when using regression for prediction, the significance level is important when using regression for hypothesis testing. Also, alpha is frequently and incorrectly confused with the constant "a" in the regression equation Y = a + bX where a is the intercept of the regression line and the Y axis. By convention, Greek letters in statistics are sometimes used when referring to a population rather than a sample. But unless you are explicitly referring to a population prediction, and your field of study follows this convention, "alpha" is not the correct term here.
