HYPOTHESIS TESTING.pptxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

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1. TEST OF HYPOTHESIS Curious mind fuels investigations, learnings, and discoveries. A human mind is always full of queries and tends to seek answers through research. Despite the many findings and discoveries, there is still a lot to be investigated and re-investigated since change is endless. For instance, one might want to know if spraying people with disinfectant lowers the spread of COVID-19, or what supplements to take to have better protection against the disease. Filipinos nowadays were also eager to know which among the available COVID-19 vaccines will be purchased, if they will decide to take one. To arrive at a decision, research requires a process known as the test of hypothesis .

2. TEST OF HYPOTHESIS In conducting a test of hypothesis, we usually use sample data to estimate what is taking place in a larger population, to which we have no access. Research questions or statements of the problem were formulated. Then, we come up with a set of testable predictions known as a hypothesis . A hypothesis is defined as a proposed explanation (may or may not be true) for a phenomenon that can be used as a basis for further verification or investigation.

3. NULL HYPOTHESIS VS. ALTERNATIVE HYPOTHESIS There are two opposing hypotheses for each phenomenon: the null hypothesis and the alternative hypothesis . The null hypothesis , represented by H , states that there is no difference between a parameter and a statistic, or that there is no difference between two parameters. On the other hand, the alternative hypothesis , represented by H a , states the existence of a difference between a parameter and a statistic or states that there is a difference between two parameters.

4. NULL HYPOTHESIS VS. ALTERNATIVE HYPOTHESIS A decision rule to resolve which of these two opposing hypotheses is more likely to be true will be followed. The null hypothesis will be rejected in favor of the alternative hypothesis if the sample evidence strongly suggests that it is false . In the same manner, if we favor the null hypothesis, we reject the alternative hypothesis .

5. TWO TYPES OF TESTS A type of test used for directional hypothesis is known as a one-tailed test . A directional (or one-tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms the week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

6. TWO TYPES OF TESTS 2. T he one used for a non-directional hypothesis is known as a two-tailed test . A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

7. RIGHT TAILED TEST VS. LEFT TAILED TEST . A one-tailed test can only be right-tailed or left-tailed , which leans in the direction of the inequality of the alternative hypothesis. To state the hypotheses, we must translate the words into mathematical symbols. The basic symbols used are as follows:

8. WHEN CAN WE USE ONE TAILED OR TWO TAILED TEST?

9. Level of Significance When one has formulated the hypothesis, the next step is to make a research design. The researcher decides on the right statistical test and selects a fitting level of significance. For example, in Scenario 1 , a sample of patients who will be given the vaccine will be chosen. After letting an appropriate period for the vaccine to be absorbed, the researcher will take each patient’s temperature.

10. Level of Significance The level of significance , usually designated by the alpha (α) symbol pertains to the degree of confidence we require in order to reject the null hypothesis in favor of the alternative hypothesis . It is the highest probability of committing a Type I error . The significance testing that we currently use is a combination of the Ronald Fisher’s idea of utilizing the probability value pa s s an index of the weight of evidence against a null hypothesis, and J erzy Neyman and Egron Pearson’s notion of testing a null hypothesis against an alternative hypothesis.

11. Level of Significance Fisher proposed that 95% is a useful threshold of confidence for only when we are 95% positive that a result is accurate should we accept it as true. In other words, if there is only a 5% chance ( α = .05) of committing an error, then we say that it is a statistically significant finding.

12. Rejection Region In testing a hypothesis, the researcher decides the level of significance to be used. The gravity of the type I error will be the basis for the level to be used. Selecting a critical value from a table, given the appropriate test, will then follow. The critical value determines the critical and non-critical regions. The symbol for critical value is C.V. Critical values had to be computed by remarkably brilliant people like Fisher. His tables for particular probability values (.05, .02 and .01) led to a trend that state test statistics as being significant in today’s well-known p < .05 and p < .01 .

13. Type I and Type II Errors The hypothesis testing procedure has four possible outcomes since the null hypothesis (H ) may be true or false, and the decision to reject or accept it is based only on the statistics taken from samples. As a result, there are two possibilities for a correct decision and two possibilities for an incorrect decision, as illustrated below.

14. Type I and Type II Errors

15. we selected 100 samples from a population in which a change exists, we would miss detecting that change in 20 of those samples.

16. Type I and Type II Errors

17. Summary

18. Summary

19. 5 POINTS EACH