paraphrase
steve912
fileparahrase.docx2. Provide suitable examples about your research (10 marks)
3. Construct null and alternative hypothesis (20 marks)
4. Suggest the method used for testing the hypothesis (10 marks)
5. Give reason for your suggestions (10 marks)
The Example is for diabetics have a mean of 100 with a standard deviation of 15. A researcher thinks that eating the dates will have a positive or negative effect on level of blood sugar. A sample of 30 patients who have always eating the dates have a mean of 140. Test the hypothesis that the dates had an effect for the diabetics.
Step 1: State the null hypothesis: H0:μ=100 (dates had an effect). Step 2: State the alternative hypothesis: H1:≠100(dates had no effect). This is a two-tailed test. Step 3: Determine level of significance. We will assume it as (0.05) indicates a 5% risk of concluding that a difference exists when there is no actual difference. so, the Critical value =1.96. Step 4: Select appreciate test. z-test is a statistical test used to determine whether two population means are different when the variances are known, and the sample size is large. We have chosen z-test because it compares a sample to a defined population and is typically used for dealing with problems relating to large samples (n > 30). Z-tests can also be helpful when we want to test a hypothesis. Generally, they are most useful when the standard deviation is known.
Step 5: Calculate z-test
Step 6: Conclusion:
As critical value (.Reject the null hypothesis, accept the research hypothesis , there is enough evidence to support the alternative hypothesis. That is mean the dates had no effect for diabetics.
The Hypothesis is a speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false.
There are two Hypothesis testing that are made Called Null hypothesis denoted (H0) which is a statistical hypothesis that assumes that the observation is due to a chance factor and other is alternative hypothesis denoted (HA/H1)which is Contrary to the null hypothesis, the alternative hypothesis shows that observations are the result of a real effect.
Hypothesis decision comes with 6 steps:
· Assume the H0 (The null hypothesis is a commonly accepted fact. It has the default, or what we would believe if the experiment were never conducted. It is the least exciting result, showing no significant difference between two or more groups. Researchers work to nullify or disprove null hypotheses.).
The HA You'll want to prove an alternative hypothesis. This is the opposite of the null hypothesis, demonstrating or supporting a statistically significant result. By rejecting the null hypothesis, you accept the alternative hypothesis.
· Select appreciate test (there are 4 types of test: C-test, T-test, Chi square- test, F-test)
· determine level of significance (also known as the alpha α) if it is not mentioned, assume it as 5%, then find the critical value from the table (Note: every test has own table)
· Collect data and calculate test statistic.
· Compare the test statistic with Critical value.
· Deciding and interpreting the result weather (reject or accept).
These decisions include deciding if we should accept the null hypothesis or if we should reject the null hypothesis. If it is not mentioned the Confidence level directly, we assume it as 5% then we find the critical value from the table.
Every test in hypothesis testing produces the significance value for that test. In Hypothesis testing,
If test statistic < critical value, then we accept the null hypothesis and reject the alternative hypothesis so that there is not enough evidence to support the alternative hypothesis so failing to reject the null hypothesis in favor of the alternative.
If the If test statistic >= critical value, then we should reject the null hypothesis so there is enough evidence to support the alternative hypothesis so reject the null hypothesis in favor of the alternative, For example, if we want to see the degree of relationship between two stock prices and the significance value of the correlation coefficient is greater than the predetermined significance level, then we can accept the null hypothesis and conclude that there was no relationship between the stock prices. However, due to the chance factor, it shows a relationship between the variables.
· Two types of Errors can result from a hypothesis test:
Type I error: When we reject the null hypothesis, although that hypothesis was true. Type I error is denoted by alpha. In hypothesis testing, the normal curve that shows the critical region is called the alpha region.
Type II errors: When we accept the null hypothesis, but it is false. Type II errors are denoted by beta. In Hypothesis testing, the normal curve that shows the acceptance region is called the beta region.
All statistical hypothesis tests have a chance of making either of these types of errors. False findings or false discoveries are more than possible; they are probable.
· One-Tailed and Two-Tailed Tests
A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test
A test of a statistical hypothesis, where the region of rejection is on both sides of the sampling distribution, is called a two-tailed test.
Finally. Hypothesis testing is one of the most important concepts in statistics because it is how you decide if something really happened, or if certain treatments have positive effects, or if groups differ from each other or if one variable predicts another. In short, you want to proof if your data is statistically significant and unlikely to have occurred by chance alone. A hypothesis test is a test of significance. Once the statistics are collected and you test your hypothesis against the likelihood of chance, you draw your conclusion. If you reject the null hypothesis, you are claiming that your result is statistically significant and that it did not happen by luck or chance. As such, the outcome proves the alternative hypothesis. If you fail to reject the null hypothesis, you must conclude that you did not find an effect or difference in your study. For example, this method is many pharmaceutical drugs and medical procedures are tested.
