This is a summary of the key concepts in scientific research and an attempt to erase some common misconceptions in science. Researchers organize their research by formulating and defining a research problem. This helps them focus the research process so that they can draw conclusions reflecting the real world in the best possible way. Normally, the null hypothesis represents the current view/explanation of an aspect of the world that the researcher wants to challenge. Research methodology involves the researcher providing an alternative hypothesis, a research hypothesis. The researcher tests the hypothesis to disprove the null hypothesis, not because he/she loves the research hypothesis, but because it would mean coming closer to finding an answer to a specific problem.

The research hypothesis is often based on observations that evoke suspicion that the null hypothesis is not always correct. The null hypothesis was that the personality determined whether a person would hurt another person, while the research hypothesis was that the role, instructions and orders were much more important in determining whether people would hurt others. Some variables change easily, like the stock exchange value, while other variables are almost constant, like the name of someone. If it is a sunny day, chances are that the temperature will be higher than if it's cloudy. Another thing that can make the temperature change is whether something has been done to manipulate the temperature, like lighting a fire in the chimney.

The independent variable is the variable which the researcher would like to measure the cause , while the dependent variable is the effect or assumed effect , dependent on the independent variable. What is the effect of personality on helping behavior? in explorative research methodology, e.g. They might not be stated because the researcher does not have a clear idea yet on what is really going on. Confounding variables are variables with a significant effect on the dependent variable that the researcher failed to control or eliminate sometimes because the researcher is not aware of the effect of the confounding variable.

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The key is to identify possible confounding variables and somehow try to eliminate or control them. Operationalization is to take a fuzzy concept conceptual variables , such as 'helping behavior ', and try to measure it by specific observations, e.g. The selection of the research method is crucial for what conclusions you can make about a phenomenon. It affects what you can say about the cause and factors influencing the phenomenon. It is also important to choose a research method which is within the limits of what the researcher can do.

Time, money, feasibility, ethics and availability to measure the phenomenon correctly are examples of issues constraining the research. Choosing the scientific measurements are also crucial for getting the correct conclusion. Some measurements might not reflect the real world, because they do not measure the phenomenon as it should. The significance test can show whether the null hypothesis is more likely correct than the research hypothesis.

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Research methodology in a number of areas like social sciences depends heavily on significance tests. A significance test may even drive the research process in a whole new direction, based on the findings. The t test also called the student's t test is one of many statistical significance tests, which compares two supposedly equal sets of data to see if they really are alike or not. The t test helps the researcher conclude whether a hypothesis is supported or not. Drawing a conclusion is based on several factors of the research process, not just because the researcher got the expected result.

It has to be based on the validity and reliability of the measurement, how good the measurement was to reflect the real world and what more could have affected the results. The observations are often referred to as 'empirical evidence ' and the logic/thinking leads to the conclusions. Anyone should be able to check the observation and logic, to see if they also reach the same conclusions. Errors of the observations may stem from measurement problems, misinterpretations, unlikely random events etc.

Generalization is to which extent the research and the conclusions of the research apply to the real world. It is not always so that good research will reflect the real world, since we can only measure a small portion of the population at a time. Validity refers to what degree the research reflects the given research problem, while reliability refers to how consistent a set of measurements are. a key concept relevant to a discussion of research methodology is that of validity. When an individual asks, is this study valid? , they are questioning the validity of at least one aspect of the study. There are four types of validity that can be discussed in relation to research and statistics.

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Thus, when discussing the validity of a study, one must be specific as to which type of validity is under discussion. Therefore, the answer to the question asked above might be that the study is valid in relation to one type of validity but invalid in relation to another type of validity. Each type of validity has many threats which can pose a problem in a research study.

Examples, but not an exhaustive discussion, of threats to each validity will be provided. For a comprehensive discussion of the four types of validity, the threats associated with each type of validity, and additional validity issues see cook and campbell 1979. statistical conclusion validity: unfortunately, without a background in basic statistics, this type of validity is difficult to understand. According to cook and campbell 1979 , statistical conclusion validity refers to inferences about whether it is reasonable to presume covariation given a specified alpha level and the obtained variances p.

Essentially, the question that is being asked is are the variables under study related? or is variable a correlated does it covary with variable b?. If a study has good statistical conclusion validity, we should be relatively certain that the answer to these questions is yes. Examples of issues or problems that would threaten statistical conclusion validity would be random heterogeneity of the research subjects the subjects represent a diverse group this increases statistical error and small sample size more difficult to find meaningful relationships with a small number of subjects.