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Quantitative Hypotheses Testing – 7 Rules You Must Follow

Most probably, you reached this article as you want to choose a quantitative research design for your academic research. Quantitative research is all about searching and analyzing the non-textual or numerical form of data. This type of research design is known for the accuracy, generalizability, and validity of its findings. Basically, quantitative research can be conducted either by forming hypotheses (null or alternate) or by composing specific research questions. In this article, we will discuss the only quantitative research that is conducted by formulating hypothesis, at the start. It will also tell you some important rules to follow in this design of research.

What are quantitative hypotheses testing?

Quantitative hypotheses testing is the statistical method of analysis mainly used to explore the facts in predictive or cause-and-effect studies. In predictive studies, hypotheses are formulated at the beginning of research showing the facts that may be true about the area of interest. However, in cause-and-effect studies, hypotheses explore the possible relationship between the variables involved in a study. Furthermore, the whole research revolves around testing the truth in the formulated hypotheses. All in all, quantitative hypotheses testing is a statistical method of analysis where a researcher tests the pre-formulated assumption regarding the characteristics, traits, and parameters of a population.

Quantitative hypotheses testing- Rules To follow:

Rules are important to achieve excellence in attaining certain goals of the research. Thus, the following are some simple rules that can help you attain the best possible results of quantitative hypotheses testing.

Rule #1:

First, formulate the null hypothesis: Null hypotheses are a statement inquiring about the opposite situation of an educated guess. For example, if a researcher thinks that an Mg deficiency ceases the growth of plants, then the null hypothesis will state that the Mg deficiency does not cease a plant’s growth. And rule number one is to formulate it at the start of the research.

Rule #2:

Make an alternative hypothesis to see more possibilities:

Making an alternative hypothesis is important to see the given situation from all other aspects as well. Suppose your statistical analysis rejects the null hypothesis, then the alternative hypothesis still opens new doors of opportunities to solve a certain problem.

Rule #3: Select the appropriate statistical test:

This rule suggests researchers take a step forward and select an appropriate test for testing the null hypothesis or alternative hypothesis (both are reciprocal to each other in many cases). The statistical test common to find the relationship between variables at the same or different time intervals is one or two-way AVONA, respectively. However, t statistical test, Z test and many other parameter tests can also be used.

Rule #4: Set some decision rules:

Many rules are search- design specific, and researchers have to set them for making an educated decision on their own. Basically, decision rules are some specific criteria that researchers use to reach the final decisions. For example, if in an analysis, you set a certain scale for measuring the parameters, then you also have to make rules to decode results and reach the end.

Rule #5: Collect the data for testing the hypothesis:

Data collection may be done after setting decision rules and before conducting analysis. No matter when you collect the data, it is important that the data must be in quantitative form. Statistical analyses can only work on the numerical form of data. Experiments, questionnaires (close-ended) and polls are important tools for collecting quantitative data.

Rule #6: Run analysis and find significance value (p-value):

The statistical test, either performed manually or by using an AI tool, must end by giving significant value. Generally, if p ˂ 0.05, then the difference between different variables will be significant; otherwise, it will not. Using such rules, you can easily find which statement (null or alternative) can best reveal the truth about a statement under study.

Rule #7: Conclude results:

The seventh and the most important rule in quantitative hypotheses testing is to report the results without being biased. Using honest means for results reporting helps you end up the analysis successfully. However, to report your results and buy dissertation online, there are several writing services offering 24/7 hours services that you must at least try.

Final Thoughts:

In a nutshell, writing a dissertation asks you to master many skills and quantitative hypotheses testing is one of them. Thus, the article has discussed seven rules that can help you in mastering this type of research design quite effectively.

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