Hypothesis Testing Quotes

“Without hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. That can be costly, either in business dollars or for your reputation as an analyst or scientist.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“If your sample contains sufficient evidence, you can reject the null and favor the alternative hypothesis.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“a confidence interval of [176 186] indicates that we can be confident that the real population mean falls within this range.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“Confidence intervals incorporate the uncertainty and sample error to create a range of values the actual population value is likely to fall within.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“When the p-value is low, the null must go. If the p-value is high, the null will fly.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“When the p-value is greater than the significance level, your sample data don’t provide enough evidence to conclude that the effect exists.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“If the p-value is less than or equal to the significance level, you reject the null hypothesis and your results are statistically significant.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“p-values tell you how strongly your sample data contradict the null.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“P-values are the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is correct.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“P-values indicate the strength of the sample evidence against the null hypothesis. If it is less than the significance level, your results are statistically significant.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“a significance level of 0.05 signifies a 5% risk of deciding that an effect exists when it does not exist.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“In other words, it is the probability that you say there is an effect when there is no effect.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“It specifies how strongly the sample evidence must contradict the null hypothesis before you can reject the null for the entire population.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“The significance level defines how strong the sample evidence must be to conclude an effect exists in the population.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“The effect is the difference between the population value and the null hypothesis value. The effect is also known as population effect or the difference.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“The null and alternative hypotheses are always mutually exclusive.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

“You can think of the null as the default theory that requires sufficiently strong evidence in your sample to be able to reject it.”
― Jim Frost, Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions