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
Power analysis can be used to calculate statistical significance. It compares the null hypothesis with the alternative hypothesis and looks for evidence that can reject the null hypothesis.
The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.
The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis.
It is the hypothesis that is presumed true until statistical evidence in the form of a hypothesis test proves it is not true.
No. The null hypothesis is not considered correct. It is an assumption, and hypothesis testing is a consistent meand of determining whether the data is sufficiently strong to say that it may be untrue. The data either supports the alternative hypothesis or it fails to reject it. See examples in links. Also note this quote from Wikipedia: "Statistical hypothesis testing is used to make a decision about whether the data contradicts the null hypothesis: this is called significance testing. A null hypothesis is never proven by such methods, as the absence of evidence against the null hypothesis does not establish it."
A hypothesis statement consists of three parts: the null hypothesis (H0), the alternative hypothesis (Ha), and the level of significance (alpha). The null hypothesis states that there is no relationship or difference between variables, while the alternative hypothesis suggests the presence of a relationship or difference. The level of significance determines the threshold for accepting or rejecting the null hypothesis based on statistical testing.
A non-directional research hypothesis is a kind of hypothesis that is used in testing statistical significance. It states that there is no difference between variables.
The z-score is a statistical test of significance to help you determine if you should accept or reject the null-hypothesis; whereas the p-value gives you the probability that you were wrong to reject the null-hypothesis. (The null-hypothesis proposes that NO statistical significance exists in a set of observations).
Power analysis can be used to calculate statistical significance. It compares the null hypothesis with the alternative hypothesis and looks for evidence that can reject the null 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.
A significance level of 0.05 is commonly used in hypothesis testing as it provides a balance between Type I and Type II errors. Setting the significance level at 0.05 means that there is a 5% chance of rejecting the null hypothesis when it is actually true. This level is widely accepted in many fields as a standard threshold for determining statistical significance.
The observed value is unlikely to have occured purely bt chance under the null hypothesis and, as a consequence, you ought to reject the null in favour of the alternative hypothesis.
If the statistical analysis shows that the significance level is below the predetermined alpha level (cut-off value), then the hypothesis is rejected. This suggests that there is enough evidence to believe that the results are not due to random chance. If the significance level is above the alpha level, then the hypothesis is accepted, indicating that the results are not statistically significant and may be due to random variation.
The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.The value specified is usually the maximum value that the test statistic can take for a given level of statistical significance when the null hypothesis is true. This value will depend on the parameter of the chi-square distribution which is also known as its degrees of freedom.
In fact, any statistical relationship in a sample can be interpreted in two ways: ... The purpose of null hypothesis testing is simply to help researchers decide ... the null hypothesis in favour of the alternative hypothesis—concluding that there is a ...
u reject if P-Value is < significance level. so since .7712 > .10 u fail to reject! remember this: "if P is high Ho will fly nd if P is low Ho must go" Help by USman Noor
The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis. The significance level of the observation - under the null hypothesis.