Hypothesis Testing Assignment Help

Hypothesis Testing Assignment Help

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Hypothesis testing is the brainchild of Ronald Fisher, Karl Pearson, Jerzy Neyman, and Pearson’s son Egon. Our statistics assignment help experts define it as a statistical method that uses experimental data to make statistical decisions. It is basically an assumption made about a parameter of a population. Statisticsassignmentexperts.com provides professional help with hypothesis testing assignments. Our experts are both knowledgeable in this area of study and brilliant in carrying out the analysis. Do not hesitate to seek our assistance when you find your homework to be an uphill task.

Key concepts and terms used in Hypothesis testing

  • Null Hypothesis

This is a statistical assumption. It states that the observation recorded is due to some factor or chance. The null hypothesis denotes that there is no difference between the two means of a population.

  • Alternative hypothesis

An alternative hypothesis contrasts with the null hypothesis. It shows that the observations made are as a result of a real effect.

  • Level of significance

This is the degree of significance in which the researcher will reject or accept the null hypothesis. The level of significance is usually 5%. This is because achieving 100% accuracy is never possible.

  • Type I Error

Type I error is denoted by an alpha. It happens when the researcher rejects the null hypothesis, although it was true. A normal curve is usually drawn in hypothesis testing, to show the critical region, also known as the alpha region.

  • Type II errors

This type of error occurs when the statistician accepts the null hypothesis, but it is false. Type II error is usually denoted by beta. A normal curve is used to show an acceptance region known as the beta region.

  • Power

It is the probability of the researcher accepting the null hypothesis. 1-beta usually denotes the power of the analysis.

  • One-tailored test

A one tailored test is when the statistical hypothesis has one value.

  • Two-tailored test

This is a situation when the statistical hypothesis assumes a greater than or less than value.

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How to make statistical decisions using hypothesis testing

In hypothesis testing, the researcher has two options, whether to accept the null hypothesis or to reject it. A significance value must be produced for that particular test. In hypothesis testing, you should accept the null hypothesis when the significance value obtained from the test is greater than the predetermined significance level. On the other hand, you should reject the null hypothesis when the significance value is less than the predetermined one.

The process is quite simple and involves a series of steps:

  1. State the null hypothesis

This is the default and should be a commonly accepted fact. Researchers usually take as their null hypothesis, a theory that someone would believe if the experiment was never carried out. They then work to disapprove or nullify it. A null hypothesis is the least exciting result. It shows no significant difference between two or more groups.

  1. State your alternative hypothesis

An alternative hypothesis is the opposite of the null hypothesis. It should demonstrate or support a significant result. The alternative hypothesis is accepted by default when you reject the null hypothesis

  1. Determine the level of significance

The significance level may differ depending on the area of study. However, a typical one is always set at 0.05 (5%). Setting the alpha at 0.05 means that the researcher will reject the null hypothesis because there is a 5% chance that he will find support for the alternative hypothesis. However, the truth is that the null hypothesis is true, and he was wrong to reject it.

A significance level statistically shows how confident you are with your conclusion.

  1. Calculating the P-value

This is a calculated probability that indicates the chance of achieving the null hypothesis results. The P-level is the actual result of your data after calculation. If the P-value is low, then you have stronger support for your alternative hypothesis.

  1. Drawing a conclusion

You can reject the null hypothesis if the P-value meets the significance level requirements. If suppose you calculated your P-value to be 0.02 while your significance level is 0.05, then accept your alternative hypothesis and reject the null hypothesis.

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Example 1

Vitamin c is considered to have the ability to prevent and cure a common cold. A potential hypothesis test could help us understand if this is just a myth:

Null Hypothesis: Those who take vitamin C are no less likely to contract flu during the cold season.

Alternative hypothesis: Those who take vitamin C are less likely to contract flu during the cold season

Significance level:  Let us set our significance level at 0.05

P-value: calculated at 0.20

Conclusion: After drawing two samples from our population, providing one with vitamin C and the other with placebo during the cold season, record whether the participant contracted the virus during that period. Your statistical analysis of the results calculates the P-value at 0.20, which is way above the significance level of 0.05. From this experiment, there is no support for the alternative hypothesis – vitamin c can prevent flu. As a result, you accept the null hypothesis.

Example 2

Suppose we want to find out the degree of relationship between two stock prices, and the predetermined significance level is less than the value of the correlation coefficient. We accept the null hypothesis and say that no relationship exists between the two stock prices.

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We provide hypothesis testing assignment help on all topics

Our hypothesis testing coursework help caters to all areas and concepts under this subject. Our highly qualified and experienced experts can assist you with assignments related to:

  • Decision Rulet-test
  • Z-test
  • Kruskal-Wallis test
  • Hypothesis formulation
  • Power of a Test
  • Sampling theory
  • ANOVA
  • Relationship between alpha and beta
  • Testing proportion
  • Types of errors
  • Mann-Whitney U test
  • Test of one variance
  • Wilcoxon Signed-Rank Test
  • Paired-samples t-test
  • One way MANOVA

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