# Science if Conducting an Experiment Term Paper

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¶ … science if conducting an experiment that can allow the experimenter to make reasonable inferences about the material described. This paper describes different aspects of the experimental process. It discusses descriptive and inferential statistics; single case and small N. research designs; true experiments and experimental designs; and qausi-experiments. It discusses the relative strengths and weaknesses of each experimental approach.

What are the similarities between descriptive and inferential statistics? What are the differences? When should you use descriptive and inferential statistics?

Descriptive statistics refers to data that describes, shows, or summarizes data in a meaningful way (Lund Research Ltd., 2012). Descriptive statistics present the data, but they do not allow one to make conclusions about data. In other words, descriptive statistics can be described as a way to organize raw data. There are two main types of descriptive visits that are most relevant: measures of central tendency and measures of spread (Lund Research Ltd., 2012). Descriptive statistics are frequently summarized in tables, charts, and graphs, which make it easy to see the general results of a study. "Descriptive statistics are applied to populations and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population (i.e. everybody you are interested in)" (Lund Research Ltd., 2012).

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paper NOW! Inferential statistics is a means of translating descriptive statistics and trying to apply it to a large group, when one does not have access to an entire population. "Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn. It is, therefore, important the sample accurately represents the population. The process of achieving this is called sampling. Inferential statistics arise out of the fact that sampling naturally incurs sampling error and thus a sample is not expected to perfectly represent the population. The methods of inferential statistics are (1) the estimation of parameter(s) and (2) testing of statistical hypotheses" (Lund Research Ltd., 2012).

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One would use descriptive statistics to present the information received from a specific population. Descriptive statistics are clear, but they only allow one to present information about those things that were actually measured. Inferential statistics have a margin of error, but allow the researcher to make conclusions about a broader group than was actually measured.

2. What are the similarities between single-case and small-N research designs? What are the differences? When should you use single-case and small-N research designs?

Single case research design is a design that is frequently used in applied psychology, and is when a subject serves as his own control group. The goal of the single-case research design is to examine the impact of a variable on the subject. "Single-case research is idiographic rather than nomothetic" (Brogan, Unk.). There are several features of a single-subject design including: baseline assessment to determine the status quo before an intervention is applied, and continuous assessment to determine the impact of the intervention.

Many people use the term small-N research design interchangeably with single-case research designs. Small N. studies carefully manipulate a single variable to determine the impact of that variable on the subjects of the study. Clinical trials are a good example of small-N studies.

Both single case and small-N research designs involve the observation of a subject after some type of intervention. Both research designs are idiographic. However, the two research designs have some differences and are useful in different scenarios. Single case research designs are often conducted as case studies and involve the intense observation of a single subject. They have limited utility in the application of research because they only reveal how an intervention interacts with a single subject. However, in cases where something might be extremely rare, a single case design may be the only practical way to study something. In contrast, small-N research designs allow for the study of a larger group, which might be more representative of the actual population of people experiencing a particular problem.

3. What are true experiments? How are threats to… [END OF PREVIEW] . . . READ MORE

What are the similarities between descriptive and inferential statistics? What are the differences? When should you use descriptive and inferential statistics?

Descriptive statistics refers to data that describes, shows, or summarizes data in a meaningful way (Lund Research Ltd., 2012). Descriptive statistics present the data, but they do not allow one to make conclusions about data. In other words, descriptive statistics can be described as a way to organize raw data. There are two main types of descriptive visits that are most relevant: measures of central tendency and measures of spread (Lund Research Ltd., 2012). Descriptive statistics are frequently summarized in tables, charts, and graphs, which make it easy to see the general results of a study. "Descriptive statistics are applied to populations and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population (i.e. everybody you are interested in)" (Lund Research Ltd., 2012).

Download full

paper NOW! Inferential statistics is a means of translating descriptive statistics and trying to apply it to a large group, when one does not have access to an entire population. "Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn. It is, therefore, important the sample accurately represents the population. The process of achieving this is called sampling. Inferential statistics arise out of the fact that sampling naturally incurs sampling error and thus a sample is not expected to perfectly represent the population. The methods of inferential statistics are (1) the estimation of parameter(s) and (2) testing of statistical hypotheses" (Lund Research Ltd., 2012).

## Term Paper on *Science if Conducting an Experiment That Can* Assignment

One would use descriptive statistics to present the information received from a specific population. Descriptive statistics are clear, but they only allow one to present information about those things that were actually measured. Inferential statistics have a margin of error, but allow the researcher to make conclusions about a broader group than was actually measured.2. What are the similarities between single-case and small-N research designs? What are the differences? When should you use single-case and small-N research designs?

Single case research design is a design that is frequently used in applied psychology, and is when a subject serves as his own control group. The goal of the single-case research design is to examine the impact of a variable on the subject. "Single-case research is idiographic rather than nomothetic" (Brogan, Unk.). There are several features of a single-subject design including: baseline assessment to determine the status quo before an intervention is applied, and continuous assessment to determine the impact of the intervention.

Many people use the term small-N research design interchangeably with single-case research designs. Small N. studies carefully manipulate a single variable to determine the impact of that variable on the subjects of the study. Clinical trials are a good example of small-N studies.

Both single case and small-N research designs involve the observation of a subject after some type of intervention. Both research designs are idiographic. However, the two research designs have some differences and are useful in different scenarios. Single case research designs are often conducted as case studies and involve the intense observation of a single subject. They have limited utility in the application of research because they only reveal how an intervention interacts with a single subject. However, in cases where something might be extremely rare, a single case design may be the only practical way to study something. In contrast, small-N research designs allow for the study of a larger group, which might be more representative of the actual population of people experiencing a particular problem.

3. What are true experiments? How are threats to… [END OF PREVIEW] . . . READ MORE

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