Similarities and differences between descriptive and inferential statistics

¶ … Psychology

What are the similarities between descriptive and inferential statistics?

The two types of statistics are applied in researches in making judgments regarding social behaviors. Both of them help in finding out the meanings of social incidents in estimating the values of recreation activities and some materials.

What are the differences?

Descriptive statistics are tools used in describing the basic data. They provide clear explanations about the samples of research as well as their performance. They also give a qualitative analysis of data in a more straightforward way. Statistics enhances the ability of people to interpret all forms of data. They are used to analyze large volumes of data thus reducing the data into smaller sizes. This helps the reader to have an easy time when reading the data. On the other hand, inferential statistics are extremely complicated. They dig further into any simple data. They are used in making conclusions and judgments.

When should you use descriptive and inferential statistics?

Descriptive statistics are mostly used in describing a sample group that is under study. As a result, the outcomes of a research cannot be utilized to generalize a large group. Such statistics are useful in extending survey results to any larger group. Inferential statistics are useful in making estimates, generalizations, and predictions. For example, a football fan may use such statistics to find out the trends of a team's performance so that the fan can make a bet on the team.

What are the similarities between single-case and small-N research designs?

Both research designs are appropriate for conducting research in rare phenomenon. When researchers are surveying a rare condition, they always have so many participants, which they have to draw from. Researchers use Single-case designs when they are studying a single phenomenon. Results obtained from both research designs cannot be used to generalize.

What are the differences?

Neutron-psychology and clinical psychology often apply single case designs to find out the real meaning of certain social activities. Despite being single case, they integrate self-reports, clinical observations and archive data. There are two different types of a single case designs: single subject experimental and case study. In a single case research, there is limited validity. On the other hand, small-N research is an extreme of a single case research. They have an unlimited validity and are simpler than a single case.

When should you use single-case and small-N research designs?

Small-N designs are useful in psychological studies. A few areas employ small-N designs. In psychology, psychophysics uses these designs to know how the human sense works in conjunction with the prevailing physical stimuli. They are also used in analyzing behavior in experiments. Researchers use Single-case designs when they are studying a single phenomenon. These designs help researchers in demonstrating the effects of an experimental treatment using single participants; each participant is experimented each at a time.

What are true experiments?

True experiments consist of more than a single purposively designed group, random assignment, and outcomes that are commonly measured. Ethnicity and sex cannot satisfy such requirements because it is impossible to manipulate them without purpose. These designs only occur when a sample is chosen in random and assigned to comparison groups and program. If the researchers can perform the experiments using random assignments, it means that the experiment programs are true designs.

How are they different from experimental designs?

True experiments are different from experimental designs in the way in which the ethnicity, the population, and sex are designed. The internal validity experiences threats when the researcher attempts to influence the results, this implies that, the mind of the researcher is partial and makes changes on the variables so that he or she can attain the desired results.

What are quasi-experimental designs?

These are research designs commonly used in making evaluations of educational programs when a practical or a random assignment is impossible.

Why are they important?

Researchers use quasi-experimental designs when they are unable to control the participants' assignment to conditions or when it is impossible to manipulate the variables. Instead, researchers make comparisons between variable in existing groups or a group of participants that already exists after and before the occurrence of a quasi-independent variable.

How are they different from experimental designs?

Quasi-experimental research designs are mostly used in making evaluations of problems in education when it is impossible to make random assignment or a practical. These designs are prone to numerous interpretation errors even though they are commonly used. Experimental research designs are highly effective in addressing the issue of evaluation about the usefulness and impacts of a program. These designs emphasize on the importance of comparative data as the basis for making interpretations of research findings. They increase the confidence of researchers showing that the findings are the results of an innovation or program and not of an extraneous event.