P-values e.g

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(Topic 8.4 Statistical inference and hypothesis testing: Conf) HBS108 Mapa Mental sobre P-values e.g, creado por shirley.ha el 16/09/2013.
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Resumen del Recurso

P-values e.g
  1. 1. using an RCT as an example to demonstrate hypothesis testing
    1. Imagine an RCT is conducted to compare two treatments
      1. an active drug for Irritable Bowel Syndrome (IBS
        1. and a placebo
          1. All conditions are identical apart from distribution of the actual drug to the intervention group, and the placebo to the control group.
          2. it is reasonable for the null and alternative hypotheses to state:
            1. 1. H0 = the mean (ie IBS ‘score’) of the two groups are not different
              1. 2. H1 = the mean (ie IBS ‘score’) of the two groups are statistically significantly different
                1. The p-value determines whether we accept or reject the null hypothesis.
              2. 2.researchers need to statistically demonstrate that the difference obtained between the effect of the drug for IBS compared.
                1. that of the placebo is either due to chance, or that a statistically significant difference actually exists.
                  1. the null hypothesis (no difference) can be ruled out,
                    1. then the differences between the drug and placebo is most likely due to the effectiveness of the drug itself.
                    2. A
                      1. The researchers decide what significance level to use
                        1. what cut-off point will decide significance in the test they use (in this case the cut off for the p-value)
                          1. The most commonly used level of significance is 0.05.
                            1. any test resulting in a p-value equal to, or less than 0.05 would be significant.
                              1. would reject the null hypothesis in favour of the alternative hypothesis
                      2. B
                        1. P-values equal to or less than 0.05
                          1. suggest that the observed associations could be found by chance in 5 out of 100 samples
                            1. That is, the results of 5 in 100 samples are due to chance occurrence.
                      3. GOLDEN RULES
                        1. We can REJECT the null hypothesis if p ≤ 0.05.
                          1. We must ACCEPT the null hypothesis if p > 0.05.
                          2. do not simply provide you with a “Yes” or “No” answer
                            1. provide a sense of the strength of the evidence against the null hypothesis
                            2. lower the p-value, the stronger the evidence

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