2.05a a) Understand and be able to use the language of S2 Sampling and (g) understand the nature of a hypothesis test, the difference – statistical hypothesis testing, developed through a Hypothesis Tests between one-tail and two-tail tests, and the terms ‘null binomial model: null hypothesis, alternative (g) & (h) hypothesis’, ‘alternative hypothesis’, ‘significance level’, hypothesis, significance level, test statistic, 1-tail test, region (or critical and ‘rejection region)’, ‘acceptance region’ 2-tail test, critical value, critical region, acceptance ‘test statistic’ region, p-value. (h) formulate hypotheses and carry out a hypothesis test of a population proportion in the context of a single observation Hypotheses should be stated in terms of parameter from a binomial distribution, using direct evaluation of values (where relevant) and the meanings of symbols binomial probabilities should be stated. For example, the population proportion in favour of the “,, whereis resolution”. Conclusions should be stated in such a way as to reflect the fact that they are not certain. For example, "There is evidence at the 5% level to reject . It is likely that the mean mass is less than 500 g." "There is no evidence at the 2% level to reject . There is no reason to suppose that the mean journey time has changed." Some examples of incorrect conclusion are as follows: "is rejected. Waiting times have increased." "Accept . Plants in this area have the same height as plants in other areas." 2.05b b) Be able to conduct a statistical hypothesis test for S2 Sampling and – the proportion in the binomial distribution and Hypothesis Tests interpret the results in context. (h)
2.05a a) Understand and be able to use the language of statistical hypothesis testing, developed through a binomial model: null hypothesis, alternative hypothesis, significance level, test statistic, 1-tail test, 2-tail test, critical value, critical region, acceptance region, p-value. Hypotheses should be stated in terms of parameter values (where relevant) and the meanings of symbols should be stated. For example, “,, whereis the population proportion in favour of the resolution”. Conclusions should be stated in such a way as to reflect the fact that they are not certain. For example, "There is evidence at the 5% level to reject . It is likely that the mean mass is less than 500 g." "There is no evidence at the 2% level to reject . There is no reason to suppose that the mean journey time has changed." Some examples of incorrect conclusion are as follows: "is rejected. Waiting times have increased." "Accept . Plants in this area have the same height as plants in other areas." S2 – Sampling and Hypothesis Tests (g) & (h) (g) understand the nature of a hypothesis test, the difference between one-tail and two-tail tests, and the terms ‘null hypothesis’, ‘alternative hypothesis’, ‘significance level’, ‘rejection region (or critical region)’, ‘acceptance region’ and ‘test statistic’ (h) formulate hypotheses and carry out a hypothesis test of a population proportion in the context of a single observation from a binomial distribution, using direct evaluation of binomial probabilities 2.05b b) Be able to conduct a statistical hypothesis test for the proportion in the binomial distribution and interpret the results in context. S2 – Sampling and Hypothesis Tests (h)
360173-mapping-guide-legacy-as-and-a2-units-7890-to-h240.docx