What does it mean if the t test shows that the results are not statistically significant quizlet?
If p is higher than 0.05, this means that your results are not statistically significant – the likelihood of getting the same result again if the relationship between the two variables is zero is actually pretty high.
What does no significant difference mean?
Perhaps the two groups overlap too much, or there just aren’t enough people in the two groups to establish a significant difference; when the researcher fails to find a significant difference, only one conclusion is possible: “all possibilities remain.” In other words, failure to find a significant difference means …
What does a not significant p-value mean?
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.
Why do we need to reject the null hypothesis?
We assume that the null hypothesis is correct until we have enough evidence to suggest otherwise. After you perform a hypothesis test, there are only two possible outcomes. When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
What is the outcome when you reject the null hypothesis when it is false?
The decision is to reject H0 when H0 is false (correct decision whose probability is called the Power of the Test)….Learning Outcomes.
| ACTION | H 0 IS ACTUALLY | … |
|---|---|---|
| True | False | |
| Do not reject H 0 | Correct Outcome | Type II error |
| Reject H 0 | Type I Error | Correct Outcome |
Why is it important to make sure you do not increase the Type I error?
A Type I error is when we reject a true null hypothesis. Lower values of α make it harder to reject the null hypothesis, so choosing lower values for α can reduce the probability of a Type I error. The consequence here is that if the null hypothesis is false, it may be more difficult to reject using a low value for α.
Which type of error is more dangerous?
Therefore, Type I errors are generally considered more serious than Type II errors. The probability of a Type I error (α) is called the significance level and is set by the experimenter.
What is worse a Type 1 or Type 2 error?
Of course you wouldn’t want to let a guilty person off the hook, but most people would say that sentencing an innocent person to such punishment is a worse consequence. Hence, many textbooks and instructors will say that the Type 1 (false positive) is worse than a Type 2 (false negative) error.
How do you fix a Type 1 error?
∎ Type I Error. If the null hypothesis is true, then the probability of making a Type I error is equal to the significance level of the test. To decrease the probability of a Type I error, decrease the significance level. Changing the sample size has no effect on the probability of a Type I error.
Is P value the same as Type 1 error?
This might sound confusing but here it goes: The p-value is the probability of observing data as extreme as (or more extreme than) your actual observed data, assuming that the Null hypothesis is true. A Type 1 Error is a false positive — i.e. you falsely reject the (true) null hypothesis.
Is P value the probability of type 1 error?
P Values Are NOT the Probability of Making a Mistake The most common mistake is to interpret a P value as the probability of making a mistake by rejecting a true null hypothesis (a Type I error). There are several reasons why P values can’t be the error rate. The null is true but your sample was unusual.
How do I calculate the P value?
If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value.
What is the p value for a 95 confidence interval?
90 and 2.50, there is just as great a chance that the true result is 2.50 as . 90). An easy way to remember the relationship between a 95% confidence interval and a p-value of 0.05 is to think of the confidence interval as arms that “embrace” values that are consistent with the data.