Have you ever looked at the stars in the sky and think about much they shine? Sorry to burst your bubble, but those stars are so far away, some of them are probably already dead. Stars are placed so deep in the universe; their light only reaches us long after they’ve expired. At first glance, you can’t really tell the difference between two objects. There are details that the naked eye can’t set apart. You need to look a little closer and dig a little deeper to find out the difference between certain things. You might even make predictions about something before you are proven otherwise.
As accurate as numbers seem, there are still times when the values presented aren’t actually valid. Sometimes presuming that the directional flow and value of certain marketing statistics are equal is actually wrong. This kind of inference is called a null hypothesis. The null hypothesis suggests that there are no inconsistencies between the chosen variables. This hypothesis is a “fact” that researchers try to disprove. It is accepted as valid information until there is evidence that nullifies it. Although it is called a null hypothesis, it doesn’t really mean it is already wrong; its context explains that it is a statement that can be nullified. Researchers work to find an alternative hypothesis to oppose it.
If something is already assumed factual, why is there a need to test it out? Maybe for accuracy, perhaps for evidence, or perhaps just for the fun of it. A null hypothesis is tested out simply because it is part of the process flow. Scientific research requires your sample to be tested so that researchers can decide between the given interpretations. Examining both the null and alternative hypotheses gives your research a sense of flawlessness. It also serves to find a conclusion for the population that the sample represents. Refusing to test your null hypothesis can render your study to be weak and disreputable.
The data of every research must be submitted and organized in a single document. They must be organized in a way that’s clear and comprehensive. When your study has many things to prove, you need to make sure that your research paper is easily understandable. If your research requires you report on the null hypothesis, you need a template to help simplify your job. Here are 10+ Null Hypothesis examples and templates you can utilize.
There is no way to fix a problem if you can’t understand it. If there are no context clues that help guide a researcher in finding a solution, the problem will stay as is, a problem. You need to make sure that the problem is easily identifiable and direct to the point. This also applies to the null hypothesis. If the assumption is unclear, this could lead to fixing the wrong problem, or worse, escalating the dilemma. An ambiguous thesis statement could also cause issues during the testing. To avoid any of this, you need to make sure that you follow the correct way of stating the null hypothesis.
When given a problem, the first thing you need to do is find the (alternative) hypothesis. The problem holds everything a researcher needs to write the interpretation. It is usually hidden within the word problem. The hypothesis comes as the expected outcome of quantitative research. If you can figure that out, you’ve got the right statement. This also means you need to figure out the average and then make a statement out of the given variables and expectations within the problem. The researcher must also write it as a declarative sentence.
Since your hypotheses usually deal with statistics and numbers, your statement must also be read in math. This means you need to present your hypothesis as a mathematical equation. To do this, you need to remember that the terms used are also represented as variables and symbols. The term “alternative hypothesis” is described as “H1, “ and the average is represented by “μ.” By using these symbols, you can formulate the correct mathematical statement for your research hypothesis. This equation helps researchers easily calculate and understand the hypothesis of your study. This also helps them pinpoint the right placements in Gantt charts and tables.
Not everything goes as planned. Not every experiment turns out the way you want it to. That is why you also need to figure out what you expect will happen if the test takes a different turn. As researchers, you need to figure out other possibilities that can come out of your research. This does make sense, though, since the null hypothesis needs to be proven wrong or inaccurate. This step is basically like figuring out the opposing results to the alternative hypothesis of your case study. The results and expectations usually suggest that the experiment reveals no different results.
Although there are times, even the best of researchers can’t figure out the next step. They might need a little help in accomplishing their team goals. But this doesn’t mean that they are admitting defeat, they are just in a phase of naivety. So what should researchers do when they have no idea what’s going to happen when the experiment is concluded? What results do they input?
Once again, your first job task is to figure out what should be expected. Only this time, your priority is what will happen if the experiment doesn’t show different results. That’s right; this time, you start with the null hypothesis (H0). You need to state your expectations when the test shows similar results. Input how no changes are to happen to the variables and statistics after your investigation. By forecasting these results and phenomena, you can formulate other possibilities.
After you point out the only given possibility, the next step is to find an alternate result. This leads to making the alternative hypothesis. It is explained that the definition of an alternative hypothesis is the expected outcome that opposes the null hypothesis. These two directly and proportionately contradict each other. This is the part of the research methodology where you combine these findings, turn it into an equation, and you’ve got the null hypothesis statement you need.
When you think you’ve got something right, you’re wrong. That’s why you should expect too much; you will be disappointed. And when it comes to research, you should try to debunk your null hypothesis.