The way we would formally set up the hypothesis test is to formulate two hypothesis statements, one that describes your prediction and one that describes all the other possible outcomes with respect to the hypothesized relationship.
Your prediction is that variable A and variable B will be related you don't care whether it's a positive or negative relationship. Then the only other possible outcome would be that variable A and variable B are not related.
Usually, we call the hypothesis that you support your prediction the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis. Sometimes we use a notation like H A or H 1 to represent the alternative hypothesis or your prediction, and H O or H 0 to represent the null case. You have to be careful here, though. In some studies, your prediction might very well be that there will be no difference or change. In this case, you are essentially trying to find support for the null hypothesis and you are opposed to the alternative.
If your prediction specifies a direction, and the null therefore is the no difference prediction and the prediction of the opposite direction, we call this a one-tailed hypothesis. For instance, let's imagine that you are investigating the effects of a new employee training program and that you believe one of the outcomes will be that there will be less employee absenteeism.
Your two hypotheses might be stated something like this: The null hypothesis for this study is: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase.
As a result of the XYZ company employee training program, there will be a significant decrease in employee absenteeism. In the figure on the left, we see this situation illustrated graphically. The alternative hypothesis -- your prediction that the program will decrease absenteeism -- is shown there. The null must account for the other two possible conditions: The figure shows a hypothetical distribution of absenteeism differences. We can see that the term "one-tailed" refers to the tail of the distribution on the outcome variable.
When your prediction does not specify a direction, we say you have a two-tailed hypothesis. For instance, let's assume you are studying a new drug treatment for depression. This is too broad as a statement and is not testable by any reasonable scientific means.
It is merely a tentative question arising from literature reviews and intuition. The research hypothesis is a paring down of the problem into something testable and falsifiable.
Scientists must generate a realistic and testable hypothesis around which they can build the experiment. Some examples could be:. Over-fishing affects the stocks of cod. If over-fishing is causing a decline in the numbers of Cod, reducing the amount of trawlers will increase cod stocks. These are acceptable statements and they all give the researcher a focus for constructing a research experiment.
Though the other one is perfectly acceptable, an ideal research hypothesis should contain a prediction, which is why the more formal ones are favored. A scientist who becomes fixated on proving a research hypothesis loses their impartiality and credibility. Statistical tests often uncover trends, but rarely give a clear-cut answer, with other factors often affecting the outcome and influencing the results.
Whilst gut instinct and logic tells us that fish stocks are affected by over fishing, it is not necessarily true and the researcher must consider that outcome. Perhaps environmental factors or pollution are causal effects influencing fish stocks.
A hypothesis must be testable , taking into account current knowledge and techniques, and be realistic. If the researcher does not have a multi-million dollar budget then there is no point in generating complicated hypotheses. A hypothesis must be verifiable by statistical and analytical means, to allow a verification or falsification. This means that the research showed that the evidence supported the hypothesis and further research is built upon that.
Be written in clear, concise language. Have both an independent and dependent variable. Be falsifiable — is it possible to prove or disprove the statement?
Make a prediction or speculate on an outcome. Be practicable — can you measure the variables in question? Hypothesize about a proposed relationship between two variables, or an intervention into this relationship. Consider the following hypotheses.
Are they likely to lead to sound research and conclusions, and if not, how could they be improved? Adding mica to a plastic compound will decrease its viscosity. Those who drink a cup of green tea daily experience enhanced wellness. Prolonged staring into solar eclipses confers extrasensory powers. A decline in family values is lowering the marriage rate. Children with insecure attachment style are more likely to engage in political dissent as adults.
This is an ideal hypothesis statement. It is well-phrased, clear, falsifiable and merely by reading it, one gets an idea of the kind of research design it would inspire.
This hypothesis is less clear, and the problem is with the dependent variable. Though this hypothesis looks a little ridiculous, it is actually quite simple, falsifiable and easy to operationalize. The obvious problem is that scientific research seldom occupies itself with supernatural phenomenon and worse, putting this research into action will likely cause damage to its participants. When it comes to hypotheses, not all questions need to be answered!
However, scientists should always be alert for their own possible biases creeping into research, and this can occur right from the start. Normative topics with moral elements are seldom neutral. A better hypothesis will remove any contentious, subjective elements.
Hypothesis. In research, a hypothesis is a suggested explanation of a phenomenon. A null hypothesis is a hypothesis which a researcher tries to disprove. Normally, the null hypothesis represents the current view/explanation of an aspect of the world that the researcher wants to challenge.
An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory (see inductive research). There is no formal hypothesis, and perhaps the purpose of the study is to explore some area more thoroughly in order to develop some specific .
The research hypothesis is a paring down of the problem into something testable and falsifiable. In the above example, a researcher might speculate that the decline in . Put simply, a hypothesis is a specific, testable prediction. More specifically, it describes in concrete terms what you expect will happen in a certain circumstance. A hypothesis is used to determine the relationship between two variables, which are the two things that are being tested.
Characteristics of hypothesis in Research Methodology Characteristics of hypothesis: Hypothesis must possess the following characteristics: Hypothesis should be clear and precise. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally. Experimental Research Methods Experimental methods are used to demonstrate causal relationships between variables.