# FAQ: Significance Thresholds - Interpreting a P-Value based on a Significance Threshold

This community-built FAQ covers the “Interpreting a P-Value based on a Significance Threshold” exercise from the lesson “Significance Thresholds”.

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## FAQs on the exercise Interpreting a P-Value based on a Significance Threshold

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I thought this lesson was a bit confusing and found it useful to rewrite it in a way that made it easier for me to understand.

Null Hypothesis: The idea that the observation in question is a fluke, and not representative of a statistically significant outcome.

Alternative Hypothesis: The idea that the observation in question is NOT a fluke.

P-Value: The probability (a value between 0 and 1) that the null hypothesis is correct. The typically noted significance threshold of 0.05 is an arbitrary value. It states that if the chance of the null hypothesis being true is 5% or less, it would mean that the P-Value is (confusingly for me) significant. Incidentally, the correlation that the alternative hypothesis is 95% correct is not implied by the p-value; the only association the p-value has is with the null hypothesis.

In the context of the question in the lesson plan, the observation is that 60% of students answering a particular question gets it correct. The expectation was that 70% of students would answer correctly.

Our null hypothesis then is that the 60% observation is a fluke, and that over a large enough sample size, we would expect that 70% of students would correctly answer the question. The alternative hypothesis is that the observation is not a fluke, and is in fact statistically significant.

If the p-value is 0.062 (6.2%), this is above our significance threshold of 5%, which means the null hypothesis holds. If the p-value is 0.013 (1.3%), this falls below our significance threshold, meaning that the probability of the null hypothesis being correct is too low to be considered the likely outcome, and is thus rejected.