It means concluding that results are statistically significant when, in reality, they came about purely by chance or because of unrelated factors. Your study may have missed key indicators of improvements or attributed any improvements to other factors instead.Ī Type I error means rejecting the null hypothesis when it’s actually true. These improvements could have arisen from other random factors or measurement errors.Ī Type II error happens when you get false negative results: you conclude that the drug intervention didn’t improve symptoms when it actually did.
But sometimes, this may be a Type II error.Įxample: Type I and Type II errorsA Type I error happens when you get false positive results: you conclude that the drug intervention improved symptoms when it actually didn’t.
Therefore, you fail to reject your null hypothesis. If your findings do not show statistical significance, they have a high chance of occurring if the null hypothesis is true.But sometimes, this may actually be a Type I error. In this case, you would reject your null hypothesis. If your results show statistical significance, that means they are very unlikely to occur if the null hypothesis is true.Since these decisions are based on probabilities, there is always a risk of making the wrong conclusion. Then, you decide whether the null hypothesis can be rejected based on your data and the results of a statistical test. The alternative hypothesis (H 1) is that the drug is effective for alleviating symptoms of the disease.The null hypothesis (H 0) is that the new drug has no effect on symptoms of the disease.Example: Null and alternative hypothesisYou test whether a new drug intervention can alleviate symptoms of an autoimmune disease. It’s always paired with an alternative hypothesis, which is your research prediction of an actual difference between groups or a true relationship between variables. Hypothesis testing starts with the assumption of no difference between groups or no relationship between variables in the population-this is the null hypothesis. Using hypothesis testing, you can make decisions about whether your data support or refute your research predictions with null and alternative hypotheses. Frequently asked questions about Type I and II errors.Trade-off between Type I and Type II errors.For example, if the average number of people who buy cheeseburgers from a fast-food chain on a Friday night at a single restaurant location is 200, a Poisson distribution can answer questions such as, "What is the probability that more than 300 people will buy burgers?" The application of the Poisson distribution thereby enables managers to introduce optimal scheduling systems that would not work with, say, a normal distribution. Many economic and financial data appear as count variables, such as how many times a person becomes unemployed in a given year, thus lending themselves to analysis with a Poisson distribution.Ī Poisson distribution can be used to estimate how likely it is that something will happen "X" number of times.Poisson distributions are used when the variable of interest is a discrete count variable.A Poisson distribution, named after French mathematician Siméon Denis Poisson, can be used to estimate how many times an event is likely to occur within "X" periods of time.
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