In statistics, a null hypothesis (H0) is a statement that assumes there is no significant difference or relationship between variables. It is the hypothesis that researchers aim to disprove or reject. On the other hand, an alternative hypothesis (Ha or H1) is a statement that contradicts the null hypothesis and suggests a significant difference or relationship between variables.
Examples of null and alternative hypotheses:
1. Null hypothesis: There is no difference in the mean heights of men and women.
Alternative hypothesis: There is a significant difference in the mean heights of men and women.
2. Null hypothesis: There is no association between smoking and lung cancer.
Alternative hypothesis: There is a significant association between smoking and lung cancer.
The P value is a statistical measure used to determine the strength of evidence against the null hypothesis. It represents the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true. A low P value suggests strong evidence against the null hypothesis, whereas a high P value indicates weak evidence.
Examples of P values:
1. P value = 0.036: In a study comparing two groups, a P value of 0.036 indicates that there is a 3.6% chance of obtaining the observed difference between groups if the null hypothesis is true. This value suggests that there is evidence against the null hypothesis and suggests a significant difference between groups.
2. P value = 0.218: In a study investigating the correlation between two variables, a P value of 0.218 indicates that there is a 21.8% chance of obtaining the observed correlation coefficient if the null hypothesis of no correlation is true. This value suggests weak evidence against the null hypothesis and indicates that there is no significant correlation between the variables.
What is a null and alternatives hypothesis.
Give two example each on a P value
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