Create a research question in the field of psychology that you are interested in

Question

Create a research question in the field of psychology that you are interested in researching. Write questions clearly. Include a brief background of the problem you are proposing and why it is important to the field of psychology. State the null and alternative hypothesis (in both words and statistical notation) need to address the research question. Choose which statistical test would be used to conduct the study and support it with research. Describe the type of data needs to be collected to conduct the study and what techniques are best for collecting data.

Explanation / Answer

Will give you brief theory of Hypothesis testing with example.

Theory :

Hypothesis testing first starts with theory. Theories are particular assumptions about the way things are. After a theory is formulated, a conceptual hypothesis is created, which is a more specific (than pure theory) prediction about the outcome of something. Next an experimental hypothesis is created. This is where definitions are operationalized so specific matters can be tested. For example, you could operationalize affection as number of hugs and kisses and other related actions. Then you statistically hypothesize in order to measure and test one of two hypotheses: the null, or H0, which represents non-effect (i.e. no difference between samples or populations, or whatever was tested), and an alternate hypothesis, H1.

The alternate hypothesis is that there is a difference, or an effect. It can be that one mean is greater than another, or that they are just not equal. So, the purpose of statistical testing is to test the truth of a theory or part of a theory. In other words, it is a way to look at predictions to see if they are accurate. To do this, researchers test the null hypothesis. We do not test the alternate hypothesis (which is what we think will happen). We do this because we base our testing on falsification logic (i.e., it only takes one example to prove a theory is wrong but conversely you cannot prove that a theory is right without infinite examples, so we look for examples where we are wrong).

Formulating the hypotheses

With hypothesis testing, the research question is formulated as two competing hypotheses: the null hypothesis and thealternative hypothesis. The null hypothesis is the default position that the effect you are looking for does not exist, and the alternative hypothesis is that your prediction is correct. The goal of hypothesis testing is to collect evidence and reject the null hypothesis if it appears unlikely to be true. In other words, if we reject the null hypothesis there is some experimental support for the alternative hypothesis (although it is important to keep in mind that we have not provedthe alternative hypothesis is true).

Here are the hypotheses for our example:

Null hypothesis

Physical exercise does not increase mood.

Alternative
hypothesis

Physical exercise increases mood.

Number of tails

Hypotheses can have a direction. In particular, a directional hypothesis not only states that an effect exists, but also states the direction of the effect. In the terminology of hypothesis testing, this is known as the number of tails of the hypothesis:

One tail

The hypothesis has an implied direction. The null hypothesis above is one-tailed, since it refers to an increase in mood.

Two tails

The hypothesis does not imply a direction. A two-tailed version of the null hypothesis above is "exercise has an impact on mood". In this case, we suspect there is a relationship between exercise and happiness, but we're not sure if the impact will be positive or negative.

Statistical significance

Due to naturally occuring variablilty, two seperate measurements (even of the same phenomenon) will almost always give different results. For example, assume I measure my happiness after a run on Monday, and I measure it again after a run on Wednesday. It would not be surprising if the results are different each time, since there are many factors that impact mood. Therefore, the goal of hypothesis testing is not to see if there is any difference between sets of measurements (there almost always will be), but rather to see if the differences are unlikely to be due to random variation. If so, we can say that our result is statistically significant. The general procedure is as follows:

Parametric vs non-parametric models

The statistical tests on the following pages can be categorized as either parametric or non-parametric. Parametric tests make certain assumptions about the nature of the underlying data, while non-parametric tests are more general. Parametric tests tend to have more statistical power than their non-parametric counterparts, so should be used when applicable. However, if their assumptions are violated, they may give incorrect or misleading results.

This choice between parametric and non-parametric models is based on the intrinsic nature of the data, and is therefore outside of the control of the experimenter. Therefore, you should always examine your data and conduct tests to verify the assumptions where appropriate.

The most common assumption for the parametric tests is that the assumption of normality. Typically, the assumption of normality applies to the sampling distribution, rather than the underlying data. This is good news since it is usually satisfied for sufficient large data sets (e.g. N > 30) due to the central limit theorem. In general, normality of the underlying data is sufficient, but not necessary, for the use of parametric tests.

Hope it helps you. Thanks and God Bless You :-)

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