![]() ![]() Violated assumptions of the test statistics Low power occurs when the sample size of the study is too small given other factors (small effect sizes, large group variability, unreliable measures, etc.). there is real covariation between the cause and effect). Experiments with low power have a higher probability of incorrectly accepting the null hypothesis-that is, committing a type II error and concluding that there is no effect when there actually is (I.e. Power is the probability of correctly rejecting the null hypothesis when it is false (inverse of the type II error rate). The most common threats to statistical conclusion validity are: ![]() Statistical conclusion validity involves ensuring the use of adequate sampling procedures, appropriate statistical tests, and reliable measurement procedures. Statistical conclusion validity concerns the qualities of the study that make these types of errors more likely. Fundamentally, two types of errors can occur: type I (finding a difference or correlation when none exists) and type II (finding no difference or correlation when one exists). This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and qualitative data. Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". ![]() ( May 2012) ( Learn how and when to remove this template message) Please help to improve this article by introducing more precise citations. This article includes a list of general references, but it lacks sufficient corresponding inline citations. ![]()
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