Hypothesis and errors.pptx type i and type ii errors

Hypothesis and Error

1. Hypothesis and Error (I and II) BY Nikhita Shrestha Bhcm 4 th sem

2. Hypothesis: A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions It includes components like variables, population and the relation between the variables.

3. Sources of hypothesis Following are the sources of hypothesis : The resemblance between the phenomenon. Observations from past studies, present-day experiences and from the competitors. Scientific theories. General patterns that influence the thinking process of people.

4. Examples of hypothesis Following are the examples of hypothesis based on their types: Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis. All lilies have the same number of petals is an example of a null hypothesis. If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

5. Functions of hypothesis Following are the functions performed by the hypothesis: Hypothesis helps in making an observation and experiments possible. It becomes the start point for the investigation. Hypothesis helps in verifying the observations. It helps in directing the inquiries in the right direction.

6. hypothesis testing Hypothesis testing is a systematic procedure for deciding whether the results of a research study support a particular theory which applies to a population. (https://latrobe.libguides.com/maths/hypothesis-testing) I n hypothesis testing, statisticians formulate two hypothesis: the null hypothesis and the alternative hypothesis. A null hypothesis determines there is no difference between two groups or conditions, while the alternative hypothesis determines that there is a difference

7. Types of hypothesis Null hypothesis The null hypothesis is the claim that there’s no effect in the population. In other words, the null hypothesis (i.e., that there is no effect) is assumed to be true until the sample provides enough evidence to reject it. Alternative hypothesis The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time. E xamples Null Hypothesis :   H : There is no difference in the salary of factory workers based on gender. Alternative Hypothesis :  H a : Male factory workers have a higher salary than female factory workers. Null Hypothesis :  H : There is no relationship between height and shoe size. Alternative Hypothesis :  H a : There is a positive relationship between height and shoe size. (https://resources.nu.edu/statsresources/hypothesis)

8. Errors in hypothesis testing While doing hypothesis testing, there is always a possibility of making the wrong decision about your hypothesis; such instances are referred to as 'errors’. There are two types of errors that you might make in the hypothesis testing process: type-I error and type-II error. A type I error occurs if a true null hypothesis is rejected (a “false positive”), while a type II error occurs if a false null hypothesis is not rejected (a “false negative”).

9. Type I error A Type I error means rejecting the null hypothesis when it’s actually true. F alse positive conclusion It means concluding that results are  statistically significant  when, in reality, they came about purely by chance or because of unrelated factors. It’s risk can be minimized through carefully planning in your study design.

10. TYPE II ERROR A Type II error means not rejecting the null hypothesis when it’s actually false. In reality, your study may not have had enough statistical power/ sensitivity to detect an effect of a certain size. False negative conclusion. This is not quite the same as “accepting” the null hypothesis, because hypothesis testing can only tell you whether to reject the null hypothesis. Instead, a Type II error means failing to conclude there was an effect when there actually was.  To reduce the risk we can increase the sample size or the significance level to increase statistical power.

11. Examples The Type I and Type II error rates influence each other. That’s because the significance level (the Type I error rate) affect statistical power, which is inversely related to the Type II error rate. Example: Type I vs Type II error You decide to get tested for COVID-19 based on mild symptoms. There are two errors that could potentially occur: Type I error (false positive):  the test result says you have coronavirus, but you actually don’t. (an investigator rejects a null hypothesis that is actually true in the population) Type II error (false negative):  the test result says you don’t have coronavirus, but you actually do. (the investigator fails to reject a null hypothesis that is actually false in the population.)

12. Probability of making type I and type ii errors

13. Type I and Type II errors occur where these two distributions overlap. The blue shaded area represents alpha, the Type I error rate, and the green shaded area represents beta, the Type II error rate. By setting the Type I error rate, you indirectly influence the size of the Type II error rate as well. It’s important to strike a balance between the risks of making Type I and Type II errors. Reducing the alpha always comes at the cost of increasing beta, and vice versa.

14. Is a Type I or Type II error worse? For statisticians, a Type I error is usually worse. In practical terms, however, either type of error could be worse depending on your research context. A Type I error means mistakenly going against the main statistical assumption of a null hypothesis. This may lead to new policies, practices or treatments that are inadequate or a waste of resources. Example: Consequences of a Type I error Based on the incorrect conclusion that the new drug intervention is effective, over a million patients are prescribed the medication, despite risks of severe side effects and inadequate research on the outcomes. The consequences of this Type I error also mean that other treatment options are rejected in favor of this intervention.

15. In contrast, a Type II error means failing to reject a null hypothesis. It may only result in missed opportunities to innovate, but these can also have important practical consequences. Example: Consequences of a Type II error If a Type II error is made, the drug intervention is considered ineffective when it can actually improve symptoms of the disease. This means that a medication with important clinical significance doesn’t reach a large number of patients who could tangibly benefit from it.