'A Hypothesis Is a Claim\r'

'A possible action is a claim Population think The sloshed monthly cell phone flower in this city is ? = $42 Population resemblance Example: The simile of adults in this city with cell phones is ? = 0. 68 States the claim or assertion to be try oned Is always about a existence parameter, not about a sample statistic Is the blow of the null possible action e. g. , The average diam of a manufactured bolt is not correspond to 30mm ( H1: ? ? 30 ) Challenges the status quo Alternative never contains the â€Å"=” indication may or may not be turn outIs for the most part the possible action that the researcher is trying to fix Is the resister of the null hypothesis e. g. , The average diameter of a manufactured bolt is not equal to 30mm ( H1: ? ? 30 ) Challenges the status quo Alternative never contains the â€Å"=”sign whitethorn or may not be proben Is generally the hypothesis that the researcher is trying to prove Is the opposite of the null hypothesis e . g. , The average diameter of a manufactured bolt is not equal to 30mm ( H1: ? ? 30 ) Challenges the status quo Alternative never contains the â€Å"=”sign May or may not be provenIs generally the hypothesis that the researcher is trying to prove If the sample average is close to the stated universe of discourse squiffy, the null hypothesis is not eliminateed. If the sample mean is far from the stated population mean, the null hypothesis is rejected. How far is â€Å"far enough” to reject H0? The unfavorable value of a try out statistic creates a â€Å" delineate in the sand” for decision making — it answers the psyche of how far is far enough. slip I break Reject a honest null hypothesis Considered a serious type of error The opportunity of a Type I Error is ? Called take of signifi put upce of the examineSet by researcher in recruit Type II Error Failure to reject a false null hypothesis The fortune of a Type II Error is ? Type I and Type II errors cannot happen at the same time A Type I error can only occur if H0 is true A Type II error can only occur if H0 is false Critical protect Approach to Testing For a two- goat test for the mean, ? know: date the critical Z values for a specified level of import ? from a dodge or computer Decision Rule: If the test statistic falls in the rejection region, reject H0 ; otherwise do not reject H0State the null hypothesis, H0 and the selection hypothesis, H1 crack the appropriate test statistic and sampling distribution Determine the critical values that divide the rejection and nonrejection regions Collect info and compute the value of the test statistic Make the statistical decision and state the managerial conclusion. If the test statistic falls into the nonrejection region, do not reject the null hypothesis H0. If the test statistic falls into the rejection region, reject the null hypothesis. convey the managerial conclusion in the context of the riddle p-Value App roach to Testing -value: Probability of obtaining a test statistic equal to or more extreme than the discover sample value given H0 is true The p-value is similarly called the observed level of significance H0 can be rejected if the p-value is less than ? Hypothesis Testing: ? outlander If the population standard deviation is unknown, you instead drop the sample standard deviation S. Because of this change, you use the t distribution instead of the Z distribution to test the null hypothesis about the mean. When using the t distribution you must assume the population you ar sampling from follows a normal distribution.All other steps, concepts, and conclusions be the same. One-Tail Tests In many cases, the alternative hypothesis focuses on a particular direction H0: ? ? 3 H1: ? < 3 This is a lower-tail test since the alternative hypothesis is think on the lower tail below the mean of 3 H0: ? ? 3 H1: ? > 3 This is an upper-tail test since the alternative hypothesis is foc used on the upper tail above the mean of 3 Proportions Sample proportion in the category of interest is denoted by p When both X and n †X are at least 5, p can be approximated by a normal distribution with mean and standard deviationPotential Pitfalls and Ethical Considerations Use randomly compile data to reduce selection biases Do not use human subjects without informed consent favor the level of significance, ? , and the type of test (one-tail or two-tail) to begin with data collection Do not wage â€Å"data snooping” to choose between one-tail and two-tail test, or to go out the level of significance Do not invest â€Å"data cleansing” to hide observations that do not support a stated hypothesis level all pertinent findings including both statistical significance and practical importance\r\n'