Hypothesis testing

# Hypothesis testing - Hypothesis testing decide whether data...

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Hypothesis testing: decide whether data collected supports or opposes stated parameter o Ho= null hypothesis. has benefit of the doubt o H1≠alternative hypothesis. burden of proof Steps for Hypothesis testing: o Assume H0 is true o Determine whether 1(upper or lower) or 2(difference) tailed test o Using Alpha score, use the table to see where the Ho rejection region is o Calculate Z score o If z score is in rejection region, reject ho and yes there is evidence o If z score is not in rejection region, accept ho and no there is not evidence Alpha- use .05 if not stated in problem Confidence Interval Method : o If 1 tailed test: double alpha score (so have alpha score on both sides of the graph) and then subtract the total percentage from 100 to get confidence interval. 100-(Alpha*2) o If 2 tailed:divide the alpha in half then place on both sides of the graph and then subtract from 100 to get confidence interval. 100-[(Alpha/2)*2] or 1-alpha o If given the confidence interval, to test if e is right do: (upper limit-lower limit)/2 o

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Rules of Hypothesis Testing: 1. Hypothesis testing always deals with a population. ONLY deals with average or proportion 2. Equal sign will always go with a null hypothesis. There are only 3 different hypothesis statements for mean or proportion. Difference, upper tail, or lower tail 3. What is being asked in the question ALWAYS refers to H1. Determine alternative hypothesis and then back into null hypothesis o Is there evidence that its less than/greater than o Be careful of words like EXAGGERATED since it can signify both lower/upper 4. Ho is always true unless proven otherwise 5. Always phrase your answer in terms of Ho. You are testing something about the null hypothesis o At the X significance level, there (is..reject/ is not…accept) evidence that the true (mean/proportion)…wording

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