Statistics For Dummies, 2nd Edition (For Dummies (Lifestyle))

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Understanding how to get a p-value from a test statistic is essential for assessing whether the results of your test are likely to have occurred by chance, assuming the null hypothesis is true. However, this may lead you to wonder whether it’s okay to say “Accept Ho” instead of “Fail to reject Ho.” The answer is a big no. Use the z -table to find where the row for 1.5 intersects with the column for 0.00, which is 0.9332. Because the z -table gives you only "less than" probabilities, subtract P( Z< 1.5) from 1 (remember that the total probability for the normal distribution is 1.00, or 100%): P ( Z ≥ 1.5) = 1 – P ( Z< 1.5) When designing a study, the sample size is an important consideration because the larger the sample size, the more data you have, and the more precise your results will be (assuming high-quality data). If you know the level of precision you want (that is, your desired margin of error), you can calculate the sample size needed to achieve it. You randomly sample some delivery times and run the data through the hypothesis test, and your p-value turns out to be 0.001, which is much less than 0.05. In real terms, there is a probability of 0.05 that you will mistakenly reject the pizza place’s claim that their delivery time is less than or equal to 30 minutes.

In this case you need to estimate them with the sample standard deviations, s1 and s2. The second situation is when the sample sizes are small (less than 30). In this case you can’t be sure whether your data came from a normal distribution. If the results are likely to have occurred under the claim, then you fail to reject Ho (like a jury decides not guilty). If the results are unlikely to have occurred under the claim, then you reject Ho (like a jury decides guilty). The cutoff point between rejecting Ho and failing to reject Ho is another whole can of worms that I dissect in the next section (no pun intended).Comparing Figures (a) and (c), you see Figure (a) is nearly a perfect uphill straight line, and Figure (c) shows a very strong uphill linear pattern (but not as strong as Figure (a)). Figure (b) is going downhill, but the points are somewhat scattered in a wider band, showing a linear relationship is present, but not as strong as in Figures (a) and (c). Figure (d) doesn’t show much of anything happening (and it shouldn’t, since its correlation is very close to 0). Statistics For Dummies is for everyone who wants to sort through and evaluate the incredible amount of statistical information that comes to them on a daily basis. (You know the stuff: charts, graphs, tables, as well as headlines that talk about the results of the latest poll, survey, experiment, or other scientific study.) This book arms you with the ability to decipher and make important decisions about statistical results, being ever aware of the ways in which people can mislead you with statistics. Get the inside scoop on number-crunching nuances, plus insight into how you can Hypothesis tests are used to test the validity of a claim that is made about a population. This claim that’s on trial, in essence, is called the null hypothesis (H0). The alternative hypothesis (Ha) is the one you would believe if the null hypothesis is concluded to be untrue. Learning how to find the p-value in statistics is a fundamental skill in testing, helping you weigh the evidence against the null hypothesis. where t* is the critical value from the t-distribution with n1 + n2 – 2 degrees of freedom; n1 and n2 are the two sample sizes, respectively; and s1 and s2 are the two sample standard deviations. This t*-value is found on the following t-table by intersecting the row for df = n1 + n2 – 2 with the column for the confidence level you need, as indicated by looking at the last row of the table.

For example, suppose you work for the Department of Natural Resources and you want to estimate, with 95 percent confidence, the mean (average) length of all walleye fingerlings in a fish hatchery pond. The significance level can be thought of as the highest possible p-value that would reject Ho and declare the results statistically significant. Following are the general rules for making a decision about Ho based on a p-value: Then, go to the top of the columns containing the two t-values from Step 2. The right-tail (greater-than) probability for your t-value is somewhere between the two values at the top of these columns. For example, your t = 1.60 is between t-values 1.44 and 1.94 (df = 6); so the right tail probability for your t is between 0.10 (column heading for t = 1.44); and 0.05 (column heading for t = 1.94).The t*-value comes from a t-distribution with 10 – 1 = 9 degrees of freedom. This t*-value is found by looking at the t-table. Look in the last row where the confidence levels are located, and find the confidence level of 95 percent; this marks the column you need. Then find the row corresponding to df = 9. Intersect the row and column, and you find t* = 2.262. This is the t*-value for a 95 percent confidence interval for the mean with a sample size of 10. (Notice this is larger than the z*-value, which would be 1.96 for the same confidence interval.)