Examples Of Null Hypothesis
A hypothesis is a speculation or theory based off of insufficient evidence which can later be tested to be proven true or false. An example of a hypothesis would be testing if one type of drug performed better to prevent seizures than the other. A Null hypothesis is a hypothesis that says there is no statistical significance between variables in a given hypothesis. An example of a null hypothesis would be there is no statistical relationship between which type of drug used and the amount of prevented seizures. An alternative hypothesis is the opposite of a null hypothesis. An example of the alternative hypothesis would be that there is a relationship between the number of prevented seizures and which of the drugs that are being administered.
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The P-Value tells us the probability of getting the observed value of the test statistic or a value with even greater evidence against it if the null hypothesis is actually true. What determines whether P-Value is high or low is the significance level which is also denoted as alpha. If the P-Value is lower than the alpha there is a statistically significant difference between groups and the null hypothesis is rejected for the alternative hypothesis. When the P-Value is higher than the alpha the difference between groups is not considered statistically significant and the null hypothesis is not rejected. The smaller the alpha the more stringent the test would be but normally alpha is set at 5%.
Anytime you reject a hypothesis there is a chance you made a mistake. You could have rejected a hypothesis that is true or failed to reject a hypothesis that is wrong.
Type I error is when you incorrectly rejected a null hypothesis (Berkeley, n.d.). For example, a researcher says there is a difference between drugs and their performance when in reality there is not. This can also be considered to be a false positive. The probability of making a type one error is known as alpha.
Type II error is when you failed to reject the null hypothesis when you should have. For example, a researcher says there is no difference between drugs and their performance when in reality there is a difference.
The P-Value tells us the probability of getting the observed value of the test statistic or a value with even greater evidence against it if the null hypothesis is actually true. What determines whether P-Value is high or low is the significance level which is also denoted as alpha. If the P-Value is lower than the alpha there is a statistically significant difference between groups and the null hypothesis is rejected for the alternative hypothesis. When the P-Value is higher than the alpha the difference between groups is not considered statistically significant and the null hypothesis is not rejected. The smaller the alpha the more stringent the test would be but normally alpha is set at 5%.
Anytime you reject a hypothesis there is a chance you made a mistake. You could have rejected a hypothesis that is true or failed to reject a hypothesis that is wrong.
Type I error is when you incorrectly rejected a null hypothesis (Berkeley, n.d.). For example, a researcher says there is a difference between drugs and their performance when in reality there is not. This can also be considered to be a false positive. The probability of making a type one error is known as alpha.
Type II error is when you failed to reject the null hypothesis when you should have. For example, a researcher says there is no difference between drugs and their performance when in reality there is a difference.