However, there are some important things to consider when building a compelling hypothesis.

OBSERVATION is first step, so that you know how you want to go about your research. HYPOTHESIS is the answer you think you’ll find. PREDICTION is your specific belief about the scientific idea: If my hypothesis is true, then I predict we will discover this. CONCLUSION is the answer that the experiment gives.

A scientific hypothesis is the initial building block in the scientific method. Many describe it as an “educated guess,” based on prior knowledge and observation. A hypothesis also includes an explanation of why the guess may be correct, according to National Science Teachers Association.

1. State the problem that you are trying to solve. Make sure that the hypothesis clearly defines the topic and the focus of the experiment.
2. Try to write the hypothesis as an if-then statement.
3. Define the variables.

Developing a hypothesis

1. Ask a question. Writing a hypothesis begins with a research question that you want to answer.
2. Do some preliminary research.
3. Formulate your hypothesis.
4. Refine your hypothesis.
5. Phrase your hypothesis in three ways.
6. Write a null hypothesis.

A hypothesis is a formal tentative statement of the expected relationship between two or more variables under study. • • A hypothesis helps to translate the research problem and objective into a clear explanation or prediction of the expected results or outcomes of the study.

The focus of your paper is how you will go about tackling it. For example, will your paper be argumentative in nature or will it simply present research and statistics surrounding a specific topic? These are what you need to determine before you even dive into your research.

Generally, one study cannot “prove” anything, but it can provide evidence for (or against) a hypothesis. You should NOT say “the null hypothesis was accepted.” Your study is not designed to “prove” the null hypothesis (or the alternative hypothesis, for that matter).

The alternate hypothesis is just an alternative to the null. For example, if your null is “I’m going to win up to $1,000” then your alternate is “I’m going to win $1,000 or more.” Basically, you’re looking at whether there’s enough change (with the alternate hypothesis) to be able to reject the null hypothesis.

Introductory statistics classes teach us that we can never prove the null hypothesis; all we can do is reject or fail to reject it. However, there are times when it is necessary to try to prove the nonexistence of a difference between groups.

If the P-value is small, say less than (or equal to) , then it is “unlikely.” And, if the P-value is large, say more than , then it is “likely.” If the P-value is less than (or equal to) , then the null hypothesis is rejected in favor of the alternative hypothesis.

The F value in regression is the result of a test where the null hypothesis is that all of the regression coefficients are equal to zero. In other words, the model has no predictive capability.