PSY 325 Signature Assignment

PSY 325 Signature Assignment

PSY 325 Signature Assignment


Data plays a major role in everyday life and in various disciplines to help solve various issue. Therefore, statistics helps in making sense of  and interpreting a wide range of information. People encounter a large volume of data everyday ranging from the number of people we may see per day, number of cars which drive around us in a day, the number of hours we sleep in a day, and how many people live around us. Such data may not make a lot of meaning, hence a purposeful analysis and interpretation is required, and this is where statistics comes in (Ravid, 2019). Therefore, this presentation focuses on introductory statistics, the importance of statistics in psychology and everyday life, some statistical tests and ways of representing statistical data among others.

Importance of Statistics Knowledge In Psychology

As earlier indicated, statistics is important in various fields such as psychology among other fields. Psychologists are also confronted with large volumes of data in their efforts to understand various aspects of psychology and human beings.  For example, how various variables interact and affect or impact each other, how such relationships can be measured, the strength of such relationships and the meaning of such strengths. Therefore, statistics helps in various ways. For example, through statistics, psychologists can organize data by presenting such data in a way that is easier to understand using visual displays (McGarty et al,2018). Descriptive statistics also offers a ways of summarizing facts in a way that makes it easy to understand. Inference can also be made based on data through the use of inferential statistics.

Importance of Statistics Knowledge In Every Day Life

Apart from psychology, statistics also play critical roles in everyday life and other parts of life. Possessing a strong background and knowledge of statistics and statistical methods can allow a student  do well in other subjects where statistics is needed. For example, an individual may be required to participate in research in other classes or subjects hence statistical knowledge can be of a great help. Magazines and other media usually publish and report news and scientific findings using various statistical descriptions. The implication is that it becomes easier to have a better understanding of such data and information presented when one has good statistical knowledge (McGarty et al,2018). Understanding of the statistical tests also allows individuals to make better judgments such as on health and overall living.

Difference Between Inferential and Descriptive Statistics

Descriptive ad inferential statistics have widely been used in research.  Even though both are important, they have

PSY 325 Signature Assignment
PSY 325 Signature Assignment

various differences worth exploring or explaining. Just as the name suggests, descriptive statistics helps in describing data, for example, by the use of a graph or a chart (Kaur et al.,2018). On the other hand, inferential statistics is used to make predictions or inferences from that particular data. The implication is that with inferential statistics, an individuals takes  data from a particular sample, then makes generalization. For example,  a research can ask a sample of twenty people if the like a new product brand in town. A bar chart can be made for the yes and no, to help describe the data, hence descriptive statistics. Inferential statistics can also be applied in this data by reasoning that around 80-90% of the sampled population like the new product (Kim et al.,2018).

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: PSY 325 Signature Assignment

Inferential statistics are  majorly divided into two. They include hypothesis tests which involves using a sample data to answer research questions. The other one is estimating parameters which entails obtaining statistics from a sample data and using the same to describe a population parameter. Descriptive statistics can be used for a sample data. For example,  if there is a sample data for a potential new Covid-19 drug, then descriptive statistics can be used in describing the sample through sample mean, standard deviation, formulation of boxplot or barchart and describing the shape of the sample probability distribution. For inferential statistics, a sample data is obtained from a small population and determine if the data can predict if the drug can be effective for everyone. For example, using z-scores (Oh & Pyrczak, 2023).

Description of Inferential Tests

Inferential statistics apply statistical models to help in comparing a current sample data with previous research or other data. In most cases, researchers use regression analysis, ANOVA, t-test and generalized linear model which then give straight line results or probabilities. Inferential statistics have various indexes including, regression analysis, confidence intervals, central limit theorem, T-distributions, normal distributions, hypothesis testing and binomial theorem. It is important to know the levels of measurements for the variables in data set since each level of measurement offers a different level of detail (Oh & Pyrczak, 2023) For example, while the nominal level offers least amount of detail, the ordinal levels and interval and ratio offer increasingly more details.

There are various levels of measurement. One of them is nominal level variables where there is a grouping in categories with no meaningful order. An example, is gender of the research subjects in a population, they are assigned either male or female with no hierarchy. The statistics associated with this level of measurement is percentages and frequencies. The next measurement is ordinal level of variable where there is a meaningful order. For examples assigning variables as first, second, third etc. They are also described by percentages and frequencies. The next one is interval and ratio level variables (Amrhein et al.,2019). These have more details and associated with arithmetic properties. The interval and ratio variables are described using standard deviations and means.

The Research Examples

Various research examples exist that would yield data appropriate for various inferential statistic tests. As earlier indicated, ANOVA is one of the inferential tests discussed. ANOVA  test is used to find out if the results of a research are significant, hence an individual can either reject null hypothesis or accept the alternate hypothesis (Kim et al.,2020). So a test is done to find out if there is a difference between them an example is a research involving an exploration of the impact of tea on weight loss. The researcher can then find out if there is a difference between those who did not take tea, those who took black tea and those who to green tea.  The variable is weight measured in kg. The other test is T-test. This is a test used in hypothesis testing on the basis of a difference between sample means. An example of a research is  case where researchers seek to find out if an observed mean in improvement among patients with diabetes after an intervention is due to the intervention or by chance.  Examples of variables include glycemic control, controlled blood pressure and improved physical strength.

The next test is Mann Whitney U test, which is a statistical test used when comparing the difference between independent samples that do not possess normal distribution. For the test to be used, all the variables should be in ordinal or continuous scales (Oti et al.,2021). An example is a research seeking to assess the viral load of untreated versus treated groups.  The variable involved is viral load measured in the extent of the virus. The  Wilcoxon signed rank test is a nonparametric test equivalent to dependent t-test. It is used in comparing two sets of scores that come from the same patients. For example it can be used in determining the difference in a smoker’s daily cigarette consumption before and after a one moth hypnotherapy program. The variable involved is number of cigarettes used per day measured in terms of the cigarette quantity. Linear regression entails the analysis to predict the value of a variable based on the value of another variable. It involves dependent and independent variables. Example of research include Finding out the relationship between sex and weight. The variables are sex and weight, measures as female or male and kilogram.

Levels of Measurement

The levels of measurements can be used in research. For example, the nominal data level refers to cases where values only serve as labels. An example of research is when a researcher has to categorize female and male respondents, 1 can be used for females, and 2 can be used for males. The numbers 1 and 2 do not represent any meaningful order nor any mathematical meaning (Heavrey, 2022). While they cannot be applied in performing statistical computations such as standard deviation and mean, chi-square test can be carried out on cross-tabulation of nominal data. In the ordinal data levels of measurement, the values have a meaningful order. An example is in a research involving education levels, the subjects can be categorized as high school, undergraduate or graduate degree and the categories are in order, with high school the lowest and graduate degree the highest. However, no  further arithmetic assumptions can be made. Percentages and frequencies can be used with the ordinal data levels of measurement.

Interval scale data levels of measurement can also be applied in research. It is possible to make arithmetic assumptions regarding the difference between values. An example of interval scale in research is the use of Likert scale where a researcher may ask respondents to gauge their approval of something using numbers (Grove & Cipher, 2019). For instance 5 for strongly agree, 4 for agree, 3 for neutral, 2 for disagree and 1 for strongly disagree.  The next is ration scale data levels of measurement. An example in research is a research involving obesity where the weight of the patients are recorded in various ranges, such as 50-60 kg.

Ethical Concerns For Research Examples

Research ethics plays a major role in research since the researchers have to protect the subjects’ dignity. However, during the research process, the researchers may encounter various ethical concerns. Some of the ethical issues experienced include respect for privacy, respect for anonymity and confidentiality, beneficence and informed consent. In research involving obesity, the researcher have to obtain informed consent since patient private and confidential data will be dealt with. It is also important that no physical harm or discomfort should be experienced by the research subjects. A disclosure of alternatives is also required to help protect the research subjects (West, 2020).

One of the ethical concerns here is informed consent. The research involves patient private data, hence there is a need to ensure that the patients consent so that the ethical principles are not violated. Respect for anonymity and confidentiality should also be observed in the second research. This research involves sensitive data for patients with diabetes, hence there is a need to make the data anonymous and confidential. According to the professional code they can not reveal confidential information not even to the members of the research team. It is important therefore, to seek advice in ethics committees to get approval for disseminating the results of the data collection including an account of what happened (West, 2020). In addition, they have to deal with the issue of anonymity when some features of the research make the subjects easy to identify. It is very important that nurses always bear in mind that they should protect the privacy of the patient. The trust showed to them must not be jeopardized. The research should also be carried out with the intention of benefiting the patients.

Presentation of Statistical Test

This is an example of how to present a pre and a post tests. Pre-test probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such as a disease) before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test or a negative test, respectively. In some cases, it is used for the probability of developing the condition of interest in the future (Kim et al.,2020). Test, in this sense, can refer to any medical test (but usually in the sense of diagnostic tests), and in a broad sense also including questions and even assumptions (such as assuming that the target individual is a female or male). The ability to make a difference between pre- and post-test probabilities of various conditions is a major factor in the indication of medical tests.


Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician73(sup1), 262-270.

Grove, S. K., & Cipher, D. J. (2019). Statistics for nursing research-e-book: a workbook for evidence-based practice. Elsevier Health Sciences.

Heavey, E. (2022). Statistics for nursing: A practical approach. Jones & Bartlett Learning

McGarty, C., & Haslam, S. A. (2018). Research methods and statistics in psychology. Research Methods and Statistics in Psychology, 1-584.

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine4(1), 60. Doi: 10.4103/IJAM.IJAM_7_18

Kim, M., Mallory, C., & Valerio, T. (2020). Statistics for evidence-based practice in nursing. Jones & Bartlett Publishers.

Oh, D. M., & Pyrczak, F. (2023). Making sense of statistics: A conceptual overview.

Oti, E. U., Olusola, M. O., & Esemokumo, P. A. (2021). Statistical Analysis of the Median Test and the Mann-Whitney U Test. International Journal of Advanced Academic Research7(9), 44-51.

Ravid, R. (2019). Practical statistics for educators. Rowman & Littlefield Publishers

West, E. (2020). Ethics and integrity in nursing research. Handbook of research ethics and scientific integrity, 1051-1069. Doi: 10.1007/978-3-030-16759-2_46