PSYC 241 Lecture Notes - Lecture 8: Standard Deviation, Sample Size Determination, Statistical Significance
Document Summary
Goal: rule out chance (sampling error) as a plausible explanation for results. Hypothesis testing helps determine whether a treatment has an effect. The people in the sample are measured after treatment. If people in sample are noticeably different from those in original population, we have evidence of treatment effect. It is also possible, however, that the difference between sample and population is due to sampling error. A statistical hypothesis is an assumption about a population parameter. Best way to determine whether hypothesis is true would be to examine entire population. The difference between sample and population can be explained by sampling error. The difference is too large to be due to sampling error. Much easier to show that something is false. We want to support our hypotheses, but the techniques available are better for showing that something is false. No difference between sample statistics and population parameters or. Difference between sample statistics and population parameter or.
