Answer:
a) If the null hypothesis is true, you'll get a high P-value. (it depends)
b) If the null hypothesis is true, a P-value of 0.01 will occur about 1% of the time. (false)
c) A P-value of 0.90 means that the null hypothesis has a good chance of being true. (not only has a good chance it has strong evidence)
d) A P-value of 0.90 is strong evidence that the null hypothesis is true.(true)
Step-by-step explanation:
Before i answer this question, you need to understand that p-values give you the clues to identify when you can <u>accept the null hypothesis ( null hypothesis is true)</u> and when you can r<u>eject the null hypothesis (null hypothesis is not true).</u>
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1. When you get a small p-value (typically ≤ 0.05) values that are less or equal to 0.05, for example 0.01, you reject the null hypothesis (null hypothesis is not true)
2. when you get a large p-value (> 0.05) values that are greater than 0.05, for example 0.94, 0.90. you can accept the null hypothesis because indicates weak evidence against the null hypothesis (null hypothesis is true).
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This is the explanation:</u>
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a) if the null hypothesis is true you`ll get a high p-value<u> only if the p-value is ≥ 0.05</u>
b) if p value is less or equal to 0.05. Null hypothesis is not true.
c) A P-value of 0.90 means that the null hypothesis has a good chance of being true . It not only has a good chance it is strong evidence that null hypothesis is true.
d) A p-value of 0.90 is strong evidence that null hypothesis is true. p-values that are greater than 0.05 you can accept the null hypothesis (null hypothesis is true).
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