One common mistake students make when testing hypotheses is misunderstanding the p-value.
Many people believe that a low p-value (like ) means that the null hypothesis is definitely false.
But really, a low p-value just indicates that the data does not support it.
Another mistake is not thinking about effect size. Just because a result is statistically significant, doesn’t mean it really matters in the real world.
Students often forget to check important assumptions for tests, like if the data follows a normal distribution or if variances are equal.
These assumptions can affect the results.
Lastly, some students ignore the importance of being able to repeat their findings. Just because you discover something once, it doesn’t mean it will always be the same.
Balancing these factors is important to get accurate results.
One common mistake students make when testing hypotheses is misunderstanding the p-value.
Many people believe that a low p-value (like ) means that the null hypothesis is definitely false.
But really, a low p-value just indicates that the data does not support it.
Another mistake is not thinking about effect size. Just because a result is statistically significant, doesn’t mean it really matters in the real world.
Students often forget to check important assumptions for tests, like if the data follows a normal distribution or if variances are equal.
These assumptions can affect the results.
Lastly, some students ignore the importance of being able to repeat their findings. Just because you discover something once, it doesn’t mean it will always be the same.
Balancing these factors is important to get accurate results.