Essay on Btm 8106 Complete Course Btm8106 Complete Course

.:6. Design a (hypothetical) experiment that adheres to the Scientific Method. Be sure to include all the necessary requirements at each step and give examples at all of the steps. Start with an observation, whether it’s real or made up, state the null hypothesis, and design an experiment (including an experimental and control group, random sampling, sample size, and reproducibility) that will allow the student to reject or fail to reject the hypothesis, and state the conclusion (20 points.)

designing an experiment, we have to design an experiment and we need to confirm the

3- How would you make it an experimental (rather than correlational) study (it might help to be specific here as well and define the two types of studies in your

A) Mr. Gualtieri cannot draw a conclusion about a cause-and-effect relationship from the evidence he has because he would be too quick to determine the factors that are affecting the students’ learning, development, and behavior (Ormrod, 2014, p.11). Instead of worrying about the cause-and- effect relationship from the evidence, Mr. Gualtieri should scrutinize the research report carefully; therefore, he must answer two questions. First, he must determine if he separated and controlled variables that might have an influence on the outcome. Second, he must ask if he has ruled out other possible explanations for his results? (Ormrod, 2014, p.11). If Mr. Gualtieri’s answers to both these questions are yes, then he should be able to draw a conclusion about the cause-and-effect relationship. Unfortunately, “yes” is not the answer to the two questions. This software program may not lead itself to experimental manipulation and tight control of other potentially influential variables because it is considered as a quasi-experimental study (Ormrod, 2014, p.10). Some of these influential variables that cannot be

Cohen’s paper The Earth is Round (p>0.05) is a critique of null-hypothesis significance testing (NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests ways in which researchers can improve their research, as well as the way they report their research. Cohen’s main point is that researchers who use NHST often misinterpret the meaning of p-values and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is where the researcher incorrectly accepts the false null

·  Compare the measurements in the study with the standard normal distribution, what does this tell you about the data?

1.What two factors did you investigate in your procedure, and why did you choose to compare these two factors?

Read the assigned research article for this week. Identify the descriptive statistics that are reported in the article. How can a nurse leader use descriptive statistics to justify a course of action? What descriptive statistics do you routinely use in your practice?

Instructions: This is a group activity that you will start in class and complete at home. For each of the following, note, whether the research design used is an experiment, a quasi-experiment, or a correlational approach and why. If a study is an experiment, identify the independent variable and the dependent variable. Please type your answers in complete sentences.

2. Choose one of the research questions from above and consider it in more detail. Based upon the question, what would be a reasonable hypothesis?

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

In order to know whether the evidence of research studies are accurate, one must be able to have a fundamental understanding in statistical analyses to determine if such descriptions and findings within manuscripts and articles are presented correctly and explicitly (Sullivan, 2012). Proper use of statistics begins with the understanding of both descriptive and inferential statistics. Correct organization and description of data characteristics from the population sample being studied leads the researcher to identify a hypothesis and formulate inferences about such characteristics. It is with inferential statistics that researchers conduct appropriate tests of significance and determine whether to accept or reject the identified null

4. What are the assumptions for conducting a t-test for dependent groups in a study? Which of these assumptions do you think were met by this study?

3. Determine an appropriate research design that addresses the research question regarding developmental psychology and also explain why it was chosen.

Experimental designs as in any research designs must be tested for the accuracy of the study findings (Sagepub, 2006). As such, any of the following types of validity can be used for the purpose of checking the precision of the study’s conclusions, (1) internal or sometimes referred to as causal, (2) external or generalizability of the findings, and (3) measurement. Internal validity is easier to achieve in true experiments, but may find it difficult to prove the generalizability of the study result. Generalizability is more within the confines of external validity. Thus, proving external validity is a problem with true experimental designs.