What is the role of standard error in hypothesis testing?

The standard error may fairly be taken to measure the unreliability of the sample estimate. The greater the standard error the greater the difference between observed and expected values and greater the unreliability of the sample estimate.

What is the null value in hypothesis testing?

One interpretation is called the null hypothesis (often symbolized H0 and read as “H-naught”). This is the idea that there is no relationship in the population and that the relationship in the sample reflects only sampling error.

What does it mean to reject the null hypothesis?

Rejecting the Null Hypothesis Reject the null hypothesis when the p-value is less than or equal to your significance level. Your sample data favor the alternative hypothesis, which suggests that the effect exists in the population. For a mnemonic device, remember—when the p-value is low, the null must go!

What standard error tells us?

The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.

What is the significance of standard error?

Standard error statistics measure how accurate and precise the sample is as an estimate of the population parameter. It is particularly important to use the standard error to estimate an interval about the population parameter when an effect size statistic is not available.

What type of error is made when a false null hypothesis is not rejected?

Understanding a Type II Error A type II error does not reject the null hypothesis, even though the alternative hypothesis is the true state of nature. In other words, a false finding is accepted as true.

Why is the null hypothesis always equal?

Statement of zero or no change. If the original claim includes equality (<=, =, or >=), it is the null hypothesis. If the original claim does not include equality (<, not equal, >) then the null hypothesis is the complement of the original claim. The null hypothesis always includes the equal sign.

How do you reject or accept the null hypothesis?

Rejecting or failing to reject the null hypothesis If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis.

What happens when the null hypothesis is accepted?

If we accept the null hypothesis, we will act as if the null hypothesis is true, even though we have not demonstrated that it is in fact true. This is a critical point: regardless of the results of our statistical test, we will never know if the null hypothesis is true or false.

What happens if the null hypothesis is not rejected?

Failing to reject the null indicates that our sample did not provide sufficient evidence to conclude that the effect exists. However, at the same time, that lack of evidence doesn’t prove that the effect does not exist. Capturing all that information leads to the convoluted wording!