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• Hypothesis testing: process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population
• It provides a method for understanding how reliably one can extrapolate observed findings in a sample under study to the larger
• Previous experience serves as the basis for developing hypotheses

  Hypotheses serve as the basis for developing predictions

  Predictions must be subjected to experimental or observational testing

  If the predictions are consistent with the data, they are retained, but if they are not consistent with the data, they area rejected or modified
• Previous experience serves as the basis for developing hypotheses
• Hypotheses serve as the basis for developing predictions
• Predictions must be subjected to experimental or observational testing
• If the predictions are consistent with the data, they are retained, but if they are not consistent with the data, they area rejected or modified
• False-positive error = aka Alpha error or Type I error
• False-positive error


  Occurs when you incorrectly reject the null hypothesis
• Inductive reasoning


  Since data do not provide its own interpretations, this involves statistical hypotheses testing
• False-positive error


  Occurs when you incorrectly reject the null hypothesis
• False-negative error


  Occurs when you erroneously receive a negative result and do not reject the null hypothesis
• Alpha level


  Criterion which is the highest risk of making a false-positive error that the investigator is willing to accept
• Alpha


  represents an acceptable probability of a Type I error in a statistical test
• Because alpha corresponds to a probability, it can range from 0 to 1 but is usually set at 0.05
• P value


  Probability of obtaining results as or more extreme than the observed results, assuming that the null hypothesis is correct
• A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis
• After the p value is obtained, it is compared with the alpha level previously chosen
• p value > alpha level


  Investigator fails to reject the null hypothesis
• p value < alpha level


  Investigator rejects the null hypothesis in favor of alternative hypothesis
• A normal distribution can be described by its mean and SD
• Standard error of the mean (SEM)


  Standard deviation of the sampling distribution of the mean
• the probable error of the sample mean’s estimate of true population mean
• Confidence Interval


  Refers to the probability that a population parameter will fall between a set of values for a certain proportion of times
• stable estimate


  is one that would be close to the same value if the survey were repeated
• The value of critical ratio is found on z or t table of values to determine the corresponding p value
• Critical ratios


  The ratio of an estimate of a parameter divided by the standard error (SE) of the parameter
• If your Critical Ratio falls in the Critical Region, then you reject the Null Hypothesis
• Degrees of Freedom


  Refers to the number of observations that are free to
• If your Critical Ratio does not fall in the Critical Region, then you fail to reject the Null Hypothesis