Statistical Hypothesis Testing Made Easy
Looking for article that explain statistical hypothesis testing in a simple way? We got you covered! Statistical hypothesis testing is an essential concept in statistics that helps researchers make inferences about a population based on a sample. It is a powerful tool used in research to validate or reject hypotheses and make decisions based on the results.
Statistical hypothesis testing involves making decisions about a population based on a sample. In other words, it is a method of testing whether a hypothesis about a population is true or false. It involves comparing the sample data to what we would expect if the hypothesis were true.
Steps Involved in Statistical Hypothesis Testing
Formulating the null and alternative hypotheses
The first step in statistical hypothesis testing is to formulate the null and alternative hypotheses. The null hypothesis (H0) is the hypothesis that there is no significant difference or relationship between the two groups being compared. The alternative hypothesis (Ha) is the hypothesis that there is a significant difference or relationship between the two groups. These hypotheses are formulated based on the research question and are the foundation for the entire hypothesis testing process.
Selecting the appropriate test statistic
This step involves selecting the appropriate test statistic based on the type of data being analyzed and the nature of the research question. For example, if the research question involves testing for a difference in means between two independent groups, an independent sample t-test may be appropriate. If the two groups being compared involve the same participants studied before and after a program, then you can conduct a paired sample t test in spss. If the research question involves testing for a correlation between two variables, a Pearson correlation coefficient may be appropriate. We cannot exhaust the list but this is evident that there are some rules governing the selection of test statistic.
Choosing the level of significance
The level of significance, denoted by alpha (α), is the probability of making a Type I error. It is typically set at 0.05 or 0.01, depending on the research question and the level of risk that the researcher is willing to take. A smaller alpha value indicates a higher level of confidence in the results.
Collecting the data and calculating the test statistic
This step involves collecting the data and calculating the test statistic. The test statistic is calculated based on the data collected and the selected test statistic. For example, in a t-test, the test statistic is the t-value calculated using the sample means and standard deviations.
Making a decision based on the test statistic and comparing it to the critical value
This is the final step in statistical hypothesis testing and involves making a decision based on the test statistic and compare it to the critical value. The critical value is the value beyond which the null hypothesis can be rejected.
If the test statistic is greater than the critical value, then the null hypothesis can be rejected. This means, we go with the alternative hypothesis. That is, there is sufficient evidence to support the claim.
However, If the test statistic is less than the critical value, then the null hypothesis cannot be rejected. This means that there is no sufficient evidence to support the claim.
NOTE. P-value is also used in making decisions about whether or not to reject the null hypothesis. Here, we compare the p-value and the level of significance (α).
The decision is:
Reject the null hypothesis (H0) if the p-value < α. This means that there is sufficient evidence to support the claim.
Fail to reject the null hypothesis (H0) if the p-value > α. This means that there is no sufficient evidence to support the claim.
It is worth noting that using both the critical value and p-value in making hypothesis testing decisions should lead to same findings. If you happen to use p-value and critical value and get contradicting findings, then recheck you computation of the p-value or calculation of the critical value.
Get to Know Key Statistical Hypothesis Testing Terms
There are various terms you may need to get acquinted with before doing any statistical hypothesis testing. At BestWritingBay, we understand that not only need to hire a statistician to help you with your assignment but also need to understand the key terms used in hypothesis testing. Here, you get a thorough explanation of various terms used in statistical hypothesis testing.
We have made it even easier for non-statisticians (everyone without prior statistical background) to understand how to perform statistical hypothesis testing.
Null and Alternative Hypothesis
In statistical hypothesis testing, we typically formulate two hypotheses: the null hypothesis denoted as H0 and the alternative hypothesis denoted as Ha/H1.
The null hypothesis represents the status quo, and we assume it to be true unless we have evidence to the contrary. The alternative hypothesis represents what we are trying to prove or disprove.
For example, suppose we want to test whether a new drug is effective in treating a disease. The null hypothesis would be that the drug is not effective, while the alternative hypothesis would be that the drug is effective.
Type 1 and Type II Errors
In statistical hypothesis testing, there are two types of errors that can occur: Type I and Type II errors.
Type I error occurs when we reject the null hypothesis when it is actually true.
Type II error occurs when we fail to reject the null hypothesis when it is actually false.
Level of Significance
The level of significance, denoted by alpha (α), is the probability of making a Type I error. It is usually set to 0.05, which means that there is a 5% chance of making a Type I error.
Test Statistic
The test statistic is a numerical value that measures how different the sample data is from what we would expect if the null hypothesis were true. The appropriate test statistic depends on the type of data and the nature of the hypothesis being tested.
Critical Value
The critical value is the value that separates the rejection region from the non-rejection region. It is determined by the level of significance and the degrees of freedom. However, this is different when using a normal distribution.
P-value
The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is true. A p-value less than or equal to the level of significance (α) indicates that we should reject the null hypothesis.
Applications of Statistical Hypothesis Testing
Statistical hypothesis testing is widely used in research and decision-making in various fields, including healthcare, finance, marketing, and social sciences. Here are some applications of statistical hypothesis testing:
Medical Research
In medical research, statistical hypothesis testing is used to determine whether a new drug is effective in treating a disease. It is also used to compare the efficacy of different treatments.
Quality Control
Statistical hypothesis testing is used in quality control to determine whether a process is working as intended. It is used to detect defects in manufacturing processes
Marketing
Statistical hypothesis testing is used in marketing to determine whether a new advertising campaign is effective in increasing sales or customer engagement. It is also used to test the effectiveness of different pricing strategies.
Social Sciences
In social sciences, statistical hypothesis testing is used to test theories and hypotheses about human behavior. It is used to determine whether there is a significant difference between two groups, such as in a survey or experiment.
Therefore, statistical hypothesis testing is an essential tool for making decisions based on sample data and validating or rejecting hypotheses. It is widely used in research and decision-making in various fields and helps researchers draw conclusions from data. By understanding the basics of statistical hypothesis testing and its applications, researchers can make informed decisions based on the results of their analysis.
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