Hypothesis testing is a formal process of investigating ideas or statements. In the realm of statistics, researchers use hypothesis testing quite commonly. Hypothesis testing is used to test predictions. These predictions stem from theories, and are called hypotheses. Hypothesis testing can be a tricky process as it requires caution at every stage. This article will provide a step-by-step guideline for hypothesis testing. It will also help you in understanding the process of hypothesis testing. Follow the guideline below for hypothesis testing, and you’ll be able to perform the process with great precision;
• The first step in hypothesis testing process is the statement. Hypothesis is a statement that needs to be tested using the scientific methods of hypothesis testing. It is important for the hypothesis statement to make sense. You cannot right a vague hypothesis statement. A hypothesis statement is to the point and specific. As the testing process is based on hypothesis statement, a vague statement can influence the testing process. Therefore, developing a hypothesis statement requires extensive research. You can get help from best dissetation writing services in this regard. It is important that no matter what your prediction is, it needs to be authentic.
• In research, authenticity has an important role to play. So before writing your hypothesis statement, do extensive research. This research should guide you towards the particular dimension of a subject matter that you want to explore. Once you’re done with your initial research and know what exactly it is that you want to investigate, the next step relates to hypothesis development. Under the hypothesis development, there are two statements, i.e. null and alternative hypothesis. The statement that is based on your prediction is your alternative hypothesis. Alternative hypothesis indicates the relationship between the variables that you want to investigate. So basically, you test the alternative hypothesis. Whereas null hypothesis is the statement that suggests that there is no relationship. For instance, if you want to test the relationship between height and weight of the male population;
1. Null Hypothesis: There is no relationship between the height and weight.
2. Alternative Hypothesis: There is a relationship between the height and weight.
• After hypothesis development, the next step refers to data collection. Data collection is necessary for drawing the results and finishing homework of hypothesis testing. However, testing a whole population is always a limitation. If you want to test the relationship between height and weight of the male population pertaining to UK, it is not possible to gather data for the entire population. Therefore, researchers usually collect data for a section of the population. This section is called a sample. Selecting a sample is a complex, and tedious process. There are several ways to select a sample, and this is called sampling. There are different types of sampling. The researcher can choose a sampling method based on the nature of the population, and level of convenience as well.
• A sample should be representative of the entire population. In order to make statistical inferences about the population, the sampling process is significant. A researcher must know about the different sampling methods as well. A few examples of sampling methods are random, systematic, clustered, and convenience sampling.
• Data collection is one of the most tiring jobs in the hypothesis testing process. After deciding the sampling method, the researcher selects the most suitable data collection tool. The suitability of data collection tool depends on nature of the study. For instance, investigating a phenomenon of qualitative nature may demand the selection, and implementation of interviews based method. Similarly, the quantitative nature of data can be collected through questionnaires etc.
• Once you’re able to collect data, the next step is performing a statistical test. A statistical test is based on nature of the investigation in the research. Statistical tests either provides a comparison of within-group, or between-group variance. Within-group variance statistical tests allow investigating the spread of data within a category. Aside from that, between-group variance statistical tests investigate the difference of one category from the other. These statistical tests also imply one-sample, independent sample, and matched pairs associated applications.
• Statistical tests and data analysis lead to the decision-making process of either rejecting, or accepting null hypothesis. The outcome of statistical tests indicates to either reject or fail for rejecting the null hypothesis. Usually, statistical tests generate a test statistic. This value of test static is assessed in terms of whether it falls within the critical region, or not. Another way of telling statistical significance is the p-value.
• Based on the level of significance, i.e. 1%, 5% or 10%, p-value indicates statistical significance. The level of significance is an important concept in the hypothesis testing process. Therefore, a researcher should be able to understand the errors in hypothesis testing. Errors can be of two types. These are called Type I error, and Type II error. Type I error occurs when the null hypothesis is true, but when the hypothesis is rejected. Type II error occurs when the null hypothesis is false, but the hypothesis is not rejected. Type I error is usually denoted by α. Aside from that, Type II error is donated by β.
• The last step in the hypothesis testing process is presenting findings of the analysis. The results of hypothesis testing are presented in a formal manner. These results present a summary of the data as well. A summary of the process pertaining to statistical significance with evidence (statistical test results) is presented within this context. You need to provide reasoning for choosing a particular statistical test. The relevance of the statistical test that you used is an important part of the results. After presenting these results, one should be able to discuss them as well. It can be started by describing the data first. You can see patterns in the data without even performing any tests. Discuss these patterns in your discussion section. Your discussion should also be able to comprehend the results in a layman language. Discussion on the results is important since it provides information on the phenomenon that is being invested. It is important to note that the instructions highlighted above should not be overlooked since hypothesis testing is an essential part of conducting research.