Reflection about Statistics – Essay Sample [New]
Introduction, statistics & probability, data analysis, future studies.
Learning statistics is viewed as an essential subject for every student. It is also crucial to do a reflection about statistics. The author will talk about their experience of learning the necessary skills for analyzing data and predicting possible outcomes. The statistics essay sample is going to speak on the author’s statistics and probability reflection.
I have extensively studied statistics and probability throughout this course. The probability course appeared to be a useful tool to apply in areas of statistical analysis. However, the part of learning statistics is much more prominent.
Statistical knowledge for both statisticians and non-statisticians is essential (Broers, 2006). So it is recommended that people from all fields be given the necessary statistical skills.
For that kind of reason, to gain quantitative skills to be applied and worked on in many ways, I followed this course. In this regard, I had hoped to acquire knowledge in designing experiments. I had wanted to grow in collecting and analyzing data, interpreting results, and drawing conclusions as well (Broers, 2006). I summarized and analyzed the results in my reflection about statistics and probability.
I can proudly say now that I have learned many useful things. I now understand math applications very clearly. I know how to collect, arrange, and explain the details. In data analysis, I may apply central tendency measurements such as mean, mode, and median.
I may also use dispersion measures to explain the data, such as standard deviation and variance. Furthermore, I have a detailed understanding of probability distribution. I can see what conditions are to be fulfilled for a normal distribution (Broers, 2006).
For example, I am aware of conditional probability and its applications. I also know how to use The Poisson process, Brownian motion process, Stochastic processes, Stationary processes, and Markovian processes. I learned the Ehrenfest model of diffusion, the symmetric random walk, queuing models, insurance risk theory, and Martingale theory.
Now, I can determine the relation between the two data sets. I can distinguish dependent and independent variables as well as the sort of relationship between them. I will tell you whether one random variable is causal to another. I am also able to determine positive, negative, and minimal correlations (Broers, 2006).
In most instances, data collection on an entire population is delicate (Chance, 2002). I got the necessary skills in sampling techniques in this respect. I have the skills to analyze sample data. Now I can draw inferences about the entire population using statistical probabilities and hypothesis testing.
In hypothesis testing, one seeks to determine whether the outcomes of a given sample are due to chance or known cause (Chance, 2002). Knowledge is used in the implementation of significance level, critical value, degrees of freedom, and p-value. One must be able to present the null hypothesis and the alternative hypotheses (Chance, 2002).
Now I’m able to use a t-test to assess if there are statistically significant variations between two data sets. In this regard, I understand the required assumptions for the t-test to be applied. I have a clear insight into the analysis of variance (ANOVA), both single and bidirectional. However, I feel that further practice would improve my knowledge of all of those applications (Chance, 2002).
This program has provided me with a good understanding of how statistics play a major part in life. Among other areas, statistics is the most commonly used research method in medicine, education, psychology, business, and economics (Rumsey, 2002). It helps to shape the choices people make in their everyday lives. Statistical studies may provide a clear picture of the consequences. For example, the results of such activities as smoking and contribute to corrective steps.
I was always keen to build a stable scientific career. I have now decided to major in statistics after taking this course. I would like to have advanced statistical skills that will help me to manage and evaluate complex research problems. The knowledge I’ve already obtained in this case will give me a strong foothold.
The goals I had hoped to accomplish by following this cause were well achieved. Now I can conduct experiments and collect, analyze, and interpret data. In real-life scenarios, I can apply that information and draw conclusions that will help develop answers to some issues.
Also, I have thoroughly studied and understood various causes of probability. I will be able to apply this knowledge where needed. Nevertheless, statistics will remain my main field of research.
This course, therefore, gave me the desire to seek additional statistical knowledge. For this reason, I intend to be in a better position in statistics to deal with more complex research issues.
- Broers, N. J. Learning goals: the primacy of statistical knowledge. 2006, Maastricht: Maastricht University.
- Chance, B. L. Components of statistical thinking and implications for instruction and assessment . 2002, Journal of Statistics Education, 10(3)
- Rumsey, D. J. Statistical literacy as a goal for introductory statistics courses. 2002, Journal of Statistics Education, 10(3)
❓ What should I include in my statistics reflection paper?
Your statistics essay should contain a number of research data. Thoroughly research your subject. You can also include visual support like graphs or diagrams. Make sure the statistics are broken down and provide a general image of the issue.
❓ Why are statistics so difficult?
Much of statistics makes no sense to students as it’s taught out of context. Many people often do not understand anything until they begin to examine data in their studies. You need to gain academic knowledge before you can understand statistics.
❓ How do you start a reflection on statistics?
The good idea is to start your essay on statistics with a choice of the right topic. Make sure to research everything thoroughly and take notes of interesting observations. You should have a detailed understanding of a problem, and work well with data.
❓ What is the aim of statistics?
Statistics aims to help you to use the right methods to gather the data. It ensures you use analysis correctly and present the results effectively. Statistics are essential to make science-based discoveries, make data-based decisions, and predict possible results.
❓ What is the importance of statistics and probability?
Statistics is the mathematics that we use to gather, organize, and interpret numerical information. Probability is the study of possible events. It is often used in the analysis of chance games, genetics, and weather forecasting. A myriad of other everyday occurrences can be examined.
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Reflection on Statistics Learning Goals Essay
Introduction, descriptive statistics, correlation, hypothesis testing, future plans, further study: faq.
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Statistical knowledge is important to both statisticians and non-statisticians (Broers, 2006). It is, therefore, recommended that people from all disciplines are given basic skills in statistics. For this reason, I pursued this course to obtain quantitative skills to be applied and improved on in several ways. In this regard, I hoped to obtain knowledge in designing of experiments, collection and analysis of data, interpretation of results as well as drawing of conclusions (Broers, 2006).
Today I can proudly say that my learning objectives were well met. I now have a clear understanding of statistical applications. I know how to collect, organize and describe data. I can apply measures of central tendency such as mean, mode and median in data description. I can also use measures of dispersion such as standard deviation and variance to describe data. In addition, I have clear knowledge of normal distributions as well as the conditions to be met for a distribution to be considered normal (Broers, 2006).
Through this course, am able to determine a relationship between two sets of data. I can identify dependent and independent variables and the kind of relationship that exist between them. I can tell if one variable has a causal effect on the other. Furthermore, am able to identify positive, negative and minimal correlations (Broers, 2006).
In most cases, it is difficult to collect data on a whole population (Chance, 2002). In this regard, I have obtained necessary skills in sampling techniques. I have skills to analyze data from a sample and make conclusions regarding the entire population by using statistical probabilities and test of hypothesis. In hypothesis testing, one tries to establish whether the outcomes of a certain study are due to chance or identifiable cause (Chance, 2002). Knowledge in application of significance level, critical value, degrees of freedom and p-value is used. One has to be able to formulate the null and the alternative hypotheses (Chance, 2002). I am now in a position to use t-test to determine if there are statistically significant differences between two sets of data. In this regard, I understand the required assumptions for t-test to be applied. I have a clear understanding of analysis of variance (ANOVA), both one-way and two-way. I, however, feel that more practice in all these applications will help me perfect my understanding (Chance, 2002).
This course has given me a clear understanding of the role of statistics in life. Statistics is the most used research tool in medicine, education, psychology, business and economics, among other fields (Rumsey, 2002). It helps in shaping people’s choices in their daily lives. For example, statistical findings can give a clear understanding of implications of some behaviors such as smoking and lead to corrective measures. I have always wanted to build a strong career in research. After taking this course, I have now made up my minds to major in statistics. I wish to have advanced skills in statistics which will enable me handle and analyze large and complex research problems. In this case, the knowledge I have already obtained will give me a head start.
The objectives I hoped to attain by pursuing this cause have been well achieved. Am now able to design experiments as well as collect, analyze and interpret data. I can apply this knowledge in real life situations and draw conclusions that will help find solutions to some problems. However, this course has given me energy to pursue further knowledge in statistics. For this reason, I intend to major in statistics to be in a better position to handle more complex research problems.
Broers, N. J. (2006). Learning goals: the primacy of statistical knowledge . Maastricht: Maastricht University. Chance, B. L. (2002). Components of statistical thinking and implications for instruction and assessment. Journal of Statistics Education , 10(3): 15-19. Rumsey, D. J. (2002). Statistical literacy as a goal for introductory statistics courses. Journal of Statistics Education , 10(3): 7-13
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Reflective essay about your learning experience on using statistical techniques in data analysis
Now I can determine the relation between the 400 data set. I can distingrish dependent and independent variables as well as the sort of relationship between them. I am also to determine positive and negative and minimal correla toons
Sana makatulong sa inyo
- two data sets kasi yon hndi 400
New questions in Math
- Data Collection
- Data Analysis
Is there a positive realtionship between the armspan and the height.
Through this module, I have gained more knowledge and experiences on how to interpret and analyze research data. We face quite a lot of challenges in collecting, analyzing the data, discussing the hypothesis, applying the theory to our research and the rationale of our study. However, we managed to discuss it as a group and come to a final decision of the content. Lastly, we draw conclusions and our hypothesis is accepted. On the other hand, I also learned how to use SPSS. SPSS saves time and generates results more efficiently and readily. And with the help of Statistics in Health Sciences textbook, it enables me to understand more about statistics as it contains step-by-step procedure on how to interpret the data. At first, I do not understand the reason of nurses studying Statistics. But now, I am aware that we as nurses do need to conduct Statistics research as it is very important as an evidence-based practice in the healthcare sector.
Overall, this project has been a good learning experience for me. I become more knowledgeable towards satistics and it will definetly aid me in future research because I have learnt how to analyse data, make sense of the data and draw conclusion to prove our hypothesis. Besides that, I also learnt how to input data into the SPSS and manage to view the graph and pie chart in the SPSS.
This project was a good learning experience. I have learned that according to the Vitruvian Man Model, our body part measurement are correlated with each other. And from our research study, arm span is correlated to body weight. And previously what we learnt about Research Methodology was applied to it.
Teng Wen Yang:
This project has enabled me to learn something that is slightly out of my impression of being a nurse. The scope of a nurse that I previously perceived is to care and save people. But as time progresses, nurses have to take up other roles, in this instance, doing research. Thus, having doing up this blog has allowed me to have a ‘sneak preview’ of what a real research would be like; starting from coming up with hypothesis, conceptualization to data collection and ultimately analyzing of the data collected and how we interpret the data. Using blog as a platform to publish our results has been an enriching yet time-consuming experience as I do not have any experience of blogging previously. Overall, through the process of this project, I’ve learnt how to plan out the entire process of data collection and also analysing of data collected. Adding on, this project has enabled me to better understand the purpose of this module and how it could help me as I progress. Lastly,this project has enabled me to learn the application of SPSS to data analysis does not only stop after the ICA quiz.
This module has been a good experience in research process from gathering the data to analyzing it. No doubt, my group encountered a lot of challenges in coming out with the rationale of this study and the hypothesis. This has been a preparation for my FYP in Year 3. I am also able to read data. I am able to apply statistics to research in future projects.
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- Research Reflection
- Independent Samples T-test
- Pearson’s Correlation
Reflection on Statistical Analysis
In Statistics for the Social Sciences, I have learned various techniques working with sociological data. Before this class began I was nervous that I would not understand the given material. After a couple of classes, I realized how important the information was in analyzing data. Not only is the information important, but it is also interesting to learn. I learned how to calculate data and methods by hand, in SPSS, and by R. Although calculating methods by hand is long, each step is important to find the result. Calculating data by hand and in computer software has taught me how to analyze statistics. All of the statistical methods and models have challenged my mind.
Throughout the course, I was also able to learn concepts of social statistics, testing hypotheses, and computing statistics. I was uncertain of all the statistical work that goes into research. Learning all of the material allowed me to view statistical research in a different light. The methods that I felt were the most challenging included; Z Scores, T-tests, and Chi-Square. As I found these difficult, I felt accomplished when completing the handwork. All three methods allowed me to learn the importance of comparing data to one another, understand probability, and test the significance of data. Overall, the course has helped me learn ways to use data in the various scenarios, computing data in SPSS/R, and using data for future uses.
Statistical research is important within a class, however, there is also the importance in a larger context. Statistics in social science matters in a larger context because data can be used in various ways. Research and statistics can be used in making decisions in government, education, and medical advances. Most importantly, what I have learned it can answer important questions. The methods used throughout the course can help view data in larger population scenarios.
The skills learned can also be used in a larger context in a career and a technology aspect. I do believe calculating methods by hand is very important, however, larger data sets can be analyzed by computer software. SPSS is an older system and R is a newer system. Both are used to analyze large data sets. With that being said, data is easier to analyze through computer software. The data can help people in careers who may have to analyze data as a career. It could also allow questions to be answered with large data sets to further research.
With what I have learned in this course, I will use these skills in graduate school. I will be able to research, collect, and analyze data. I could also use the skills I learned in my future career. I am pursuing a career in Clinical Mental Health Counseling after graduate school. Having a career like this would allow me to record health, depression, and or anxiety data collections. Overall, this course has and will allow me to continue to learn more about the methods, research, and statistical social science.
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- 1. Running head: Analyzing Statistics 1 Reflection of Analyzing Statistics Laura Babin, Rose Krohn, Talena Peterson, Preston Stamper, Ashley Walters Learning Team C QNT/351 September 13, 2012 Margaret Martin
- 2. ANALYZING STATISTICS 2 Reflection of Analyzing Statistics Statistics is a word commonly used in daily discussion. The presence of statistics follows society in almost every decision a person can make. For example, what college to attend based on how well previous students have done, or betting on a particular horse during a race because he is said to win (Lind, D. A., Marchal, W. G., … 2011).. Even on Wall Street financial experts are constantly checking the numbers related to stocks such as the Dow Jones or Nasdaq. Reading the numbers and percentages off of a board is the easy part; converting raw data into a numerical order or placing a value to qualify the significance is where it gets tricky. Understanding what statistics do to impact decision making is extremely prevalent in daily life; however, deciding what values to assign to particular categories or what graph best suits the information provided is where the challenge begins. There are two types of primary research: quantitative data collection and qualitative data collection. Quantitative data collected is based on numbers. For example collecting data related to peoples age or income. The information collected can be analyzed after the collection using various statistical techniques. This analysis helps the researcher create meaningful patterns and take a deeper dive into the data. The most important use of quantitative data is in hypothesis testing and can help researchers in reaching conclusions. The main methods used to collect quantitative information are using questionnaires that require input of the user’s response. The survey can be distributed using different means, such as mail, phone, and websites. Qualitative data can be collected using personal interviews, focus groups, and observations carried out for the purpose of research (Siddharth, 2011). Although personal interviews can help researchers dig into greater details, they are time consuming and expensive.
- 3. ANALYZING STATISTICS 3 Available data from the Ballard Integrated Managed Systems (BIMS) employee survey afforded us the opportunity to quantify results and make an inference as to the possible reasons for the high turnover and low morale cited by management. As such, we formulated our initial hypothesis, that poor communication was a major contributing factor. To further test our hypothesis, our consulting firm made the recommendation to continue with the approach of using an employee survey instrument after removing or refining any ambiguous questions. A quarterly collection of data will allow us to reassess the data across consistent elements while attempts are made to increase response rate, thus giving us a better sample of data to derive conclusions from. Ordinal data is a scale of measurement in which any given class is higher or lower in rank than the other. For example in the given questionnaire, five is better than four, four is better than three, three is better than two, and two is better than one. Question A, C, and D represents nominal level data. This data can be represented in any order. There is no natural order for these. For instance, for the gender question one can either report males first or the females. Question B is quantitative in nature and can be classed as ratio level data. In this level of data the zero point is meaningful because the time spent being employed at BIMS cannot be a negative number. A person could have worked for a year but not negative one year. Team C consultants have already delivered two responses based on the survey. The first response was the sample size was too small. BMIS need a much higher participation rate to gain meaningful evidence of the root causes of the problem. The second response addressed the low scores on communication. While BMIS continues administering the survey, Team C consultants can test the hypothesis of communication being a critical problem within the company. Team C consultants have compared the means of the answers to question nine on the BMIS survey. Of all 78 responses only one could not be identified as food, housekeeping, or
- 4. ANALYZING STATISTICS 4 maintenance. Of the remaining responses 32 came from food, 36 came from housekeeping, and nine came from maintenance. The mean responses were 2.03, 2.33, and 2.33 respectively. This shows the food division has the lowest scores on communication, and has the most room for improvement. There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else? Relationships between variables need to be studied and analyzed before drawing conclusions based on it. In natural science and engineering, this is usually more straightforward as you can keep all parameters except one constant and study how this one parameter affects the result under study. One variable that must be considered and compared is the response rate among the divisions. The overall response rate of 17% translates to each division where no division carried a response rate above 30%. This also supports the response of needing a larger sample size. A larger sample will help draw more accurate and complete hypotheses and conclusions.
- 5. ANALYZING STATISTICS 5 References Siddharth, K. (2011). Relationship between variables. Retrieved 13 Sep. 2012 from Experiment Resources: http://www.experiment-resources.com/relationship-between-variables.html Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Pearson-Prentice Hall.
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Basic statistical tools in research and data analysis
Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India
S Bala Bhaskar
1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]
Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].
Classification of variables
Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.
A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].
Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.
Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.
Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.
Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.
STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS
Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .
Example of descriptive and inferential statistics
The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.
Measures of central tendency
The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is
where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:
where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:
where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:
where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:
where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .
Example of mean, variance, standard deviation
Normal distribution or Gaussian distribution
Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].
Normal distribution curve
It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.
Curves showing negatively skewed and positively skewed distribution
In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.
Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).
In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]
Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]
The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].
P values with interpretation
If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]
Illustration for null hypothesis
PARAMETRIC AND NON-PARAMETRIC TESTS
Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]
Two most basic prerequisites for parametric statistical analysis are:
- The assumption of normality which specifies that the means of the sample group are normally distributed
- The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.
However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.
The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.
Student's t -test
Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:
where X = sample mean, u = population mean and SE = standard error of mean
where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.
- To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.
The formula for paired t -test is:
where d is the mean difference and SE denotes the standard error of this difference.
The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.
Analysis of variance
The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.
In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.
However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.
A simplified formula for the F statistic is:
where MS b is the mean squares between the groups and MS w is the mean squares within groups.
Repeated measures analysis of variance
As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.
As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.
When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.
As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .
Analogue of parametric and non-parametric tests
Median test for one sample: The sign test and Wilcoxon's signed rank test
The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.
This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.
If the null hypothesis is true, there will be an equal number of + signs and − signs.
The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.
Wilcoxon's signed rank test
There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.
Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.
It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.
Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.
The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.
The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.
In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]
The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]
Tests to analyse the categorical data
Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:
A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.
SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS
Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).
There are a number of web resources which are related to statistical power analyses. A few are:
- StatPages.net – provides links to a number of online power calculators
- G-Power – provides a downloadable power analysis program that runs under DOS
- Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
- SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.
It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.
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Statistical analysis essay
A statistical analysis essay is an analysis of researched statistical data. It triggers arguments to readers and there on a particular topic or subject.
The arguments are discussed and justified with values in the body of the essay.
Statistical analysis is a discussion of the collected data. It aims at explaining figures and drawing conclusions and recommendations.
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A statistical analysis essay is a form of written essay that is used to analyze statistical data. To write the analysis, you have to know some well-known overall patterns and their details.
What is a statistical analysis essay?
It’s a research paper (or report) submitted in any university or any organization to analyze any given problem or question. It explains how the data you collected has been analyzed using different statistical tools such as tables, graphs, figures and diagrams.
The output from these statistical software can be either used directly in your assignment or output will just support your writing task i.e., producing compelling arguments based on evidence collected in past/present study; also it will give decision makers a logical explanation regarding their final conclusions.
This type of paper is also called research paper (see more on research paper help )because it helps researchers to get better understanding about some phenomenon that will help to improve the quality of life or survival.
It’s also good for students who want to get involved in research projects which are conducted by their teachers or higher authority (university vice-principals, deans etc.).
It is a requirement from many universities that student should show some evidences collected during their data analysis process as well they must have clear understanding about how these evidence support/contradict with theoretical points.
It can be defined as a type of essay which focuses on providing information or description about something by using numerical data in a way similar to how it’s used for academic purposes.
The main purpose of these essays is to draw conclusions based on gathered data from individual observations.
When writing this kind of academic paper , you have to make sure that your readers are able to understand all the complex terms and expressions such as standard deviations, z-scores or correlation coefficients without any difficulties along with other mathematical solutions.
Generally speaking, there are two types of statistics application essays – descriptive papers and analytical ones.
This piece of writing is characterized with statistical content and facts, as well as some useful information that is gathered by the author while studying a topic of his/her interest.
The best thing about these essays is that you can use them for almost any purpose- whether it’s an academic assignment, or some other formal task.
You should be especially attentive when describing your findings in a clear and understandable manner, no matter how complex they are.
Being able to present data in a way that makes sense to someone else takes practice and time but once you master this skill it will make your life much easier during further projects.
In order to get better grades for your essay, you should always collect all the necessary information on the subject before starting work on it. If your task is to write a statistical analysis essay, start by reading any related literature on the subject you are interested in.
Find all the important facts about that topic you can and try to make notes while reading them so that you don’t forget anything.
Also, try to conduct some sort of research online if there’s no information available at hand- this will help you find all the useful sources that provide essential information about relevant issues.
In most cases, choosing an interesting research paper topic for your statistics application essay makes it much easier for you to focus and get better results than with something overly complicated or uninteresting.
Make sure that your chosen topic has some connection to math or statistics since these fields require being able once again to interpret data in a way that makes sense to other people.
It’s true that you can always dig deep into the subject and try to prove your point of view using various statistics, but this is highly doubtful since one of the main guidelines for these essays is to show how things are connected- not just say they are.
Where do you start writing a statistical analysis essay?
Are you tasked with writing a statistical analysis essay and wondering where to start? Below is a guide on how to begin writing your statistical analysis essay.
Begin with selecting a topic
Getting the right topic for your essay is the key to success here. But you base it on the kind of data collected. Research widely and gain an understanding of the subject before settling on the topic. The topic should be one that promises interesting statistics to your reader. It guides you through the data mining process.
When you have settled on the right topic for your essay, go into the depths mining data for your paper. The basis of a statistical analysis essay is the statistics data in hand. It is from this data that you build the body of your essay. The time taken during this stage depends on the data collection formula.
The data you gather should be accurate and reliable. It should be a representative of the population from which you drew it.
The most popular methods of data collection are;
- Direct Observation
Having all your data in place, you are ready to begin the actual writing of your statistical analysis essay. The first step in writing your essay is creating a draft. Drafting allows you to have all your ideas on paper. It gives you room to organize your points as they should appear in your final essay.
While you draft, ensure to include all parts of your essay . Like any other essay, a statistical analysis paper consists of an introduction , a body, and a conclusion . After drafting, write your essay.
How to write a statistical analysis paper – step by step
Step 1: Find a good topic
You should find a topic related to your study which you can conduct the statistical analysis from. There are several sources available for topics. You could read some research papers, journals, professional magazines or any discussion forums about the subject that interests you most.
Step 2: Get an outline of statistics paper
Although there is no particular form for writing a statistical analysis paper, following the general structure given below would help in organizing and presenting your work properly:
- Introduction : This part discusses what background information is needed on the given topic and why this study has been conducted (problem statement).
- Body : The main body gives details on how the problem was analyzed.
- Discussion : This part draws out certain conclusions based on methods used in data collection and analysis.
- Conclusion : Conclusions are drawn together based on the findings of this research and a call to future research is given.
- References : The sources used in this paper should be listed here.
Step 3: Collect Data and Analyze it with SPSS or any statistical analysis software
Collecting data for statistical analysis is only the first step towards writing a paper. You would need some knowledge about statistics, database management, database design along with statistical package like SPSS (or any other related software) for analyzing the data so that they can produce meaningful outputs while presenting them in your report.
Take care of following points when you collect and analyze the data:
- The sample size you have taken must be big enough to represent your whole population properly; otherwise your data would be considered as inadequate;
- Approach to analysis should be clear and consistent. You must have a reason for choosing one over other alternatives of statistical techniques like simple regression, multiple regression etc.;
- You should describe your data by giving different characteristics including nominal, ordinal, interval and ratio scale; what type of variables you used in data collection (qualitative or quantitative); if the variables are qualitative then their classifications i.e., how many classes/groups they consist of would be given too;
- The same discipline must also be followed while presenting the outputs from SPSS such as tables (in reports), graphs, figures and diagrams (in magazines). These represent your data graphically so that they can understand it easily.
Find out more about our spss assignment help services.
Step 4: Drafting the report (or statistical analysis essay)
The drafting of this part might be a daunting task for you because you need to follow some general rules and formatting guidelines (as described below) while writing your analysis report. You should be able to write a good research paper i.e., one that is well written, having proper structure, with coherent presentation of data & output from software etc.;
You must mention in introduction about what is being reported e.g., number of respondents or observations, and how they are distributed/grouped together;
Body paragraphs should have similar structure which would carry information based on different statistics tools used in the study like tables, graphs, figures etc.; it should present interpretation and discussion of findings as well;
Step 6: Proofreading the report
You need to cross check all grammar errors, spelling mistakes etc.; because a small mistake may contain big consequences regarding your grades or rewards; no one would read your paper due to its bad presentation. You also need to go through visual aspect closely i.e., how things are arranged on pages like size of paper, font types, margin, spacing etc.; these aspects could also affect on you grades or rewards being given to you.
Step 7: Style & Formatting of your statistical analysis report
While writing a paper, there are some general rules that should be followed; if these rules are not followed then it shows an unprofessional approach towards writing this report. Here is the list of things that must be remembered while writing or drafting a research paper or analysis report: Use proper English in formatting all parts of the draft like title page, abstract, main body and reference page; all footnotes should be given at end with double space after each line; make sure that footnotes do not affect on main flow of text between them i.e., avoid using multi-column footnotes as they may make reader confused which one to read first or last;
A statistical analysis essay can be written easily if you follow these steps for the preparation and then drafting it carefully.
Let’s look at each of the components of a statistical analysis essay.
The introduction of a statistical analysis essay should have a description of your essay topic . You can write it as a theme statement .
Secondly, we have a literature review , which is generally a report of the findings of other writers who have carried out similar researches.
The best way to write a literature review is by carrying out detailed research on your theme statement. Remember to credit the other researchers.
The body is the central part of your essay. Divide the body into several sections. The body of a statistical analysis paper comprises of;
- Methods – The data collection method used describes how the data you will present was collected. Also, give an account of why you settled on that method.
- Data presentation – You represented the data collected in the form of graphs, tables, or charts. Pick the best one to present your data best.
- Data analysis – Here, you break down your data into simpler forms for the reader.
- Discussion – In this section, you bring out your arguments pulled from the data. It is here where you express your views about that topic, as shown by the data. You discuss the analysis and the results of the research.
Your last section of the paper consists of several sentences that summarise the findings and highlight the importance of the research. You will be targeting to create a lasting impression on the reader.
You need to provide any information to support your essay and the reason why the reader should consider it.
In the recommendation section, list any areas that you would want future researchers to look into related to your research. It should be short, made up of about five points.
How to write a data analysis paper
While doing any kind of data analysis, there are three important steps that should be followed:
Step 1: Exploring the given problem or question; understand it deeply and clearly so that researcher can start analyzing the data. Sometimes students get confused why they need to do this step before actually jumping into their task of collecting information from different sources like books, magazines, scholars etc.; but here is the simple answer to this question… Without knowing the objective of your research, you cannot collect the data and without collecting the right data you can’t produce meaningful results.
It’s like a complicated task to write a descriptive text on any topic that has not been well-researched by writer; before start writing, we need to do some basic homework i.e., finding suitable information sources (books, journals etc.). Same as in statistics analysis essay/report example , there is an important step which should be performed i.e., reading related books or studies to get better understanding about sample unit or population being studied; only after this step becomes clear for writer, then they can move on next step i.e., designing their own study or test procedures to collect relevant statistical evidences.
Step 2: Gather statistical information from your research i.e., data analysis; analyst or analysts has to use different test procedures (one of computer based approach) to collect relevant statistical evidences and as result they will get some data that is also known as “statistical outputs.” For example, if researcher wants to conduct a survey, then he may have designed their own questionnaires based on the objective but after conducting the survey, they will get evidences through results from each person who took part in their study/test which will be analyzed by them using their collected information sources like recently published articles/books etc.; after going through all these process, it’s time for writer to start analyzing their findings.
Step 3: Analyzing relevant statistical data or results; this process may require several steps. For example, if researcher wants to conduct a descriptive research then they will start by collecting relevant statistical data i.e., information from each individual or group who participated their study/test and after that it’s time for writer to perform first task which is “describing” the given data via graphs and charts; these are also known as “visual representations.” After creating those tools/devices for analyzing collected results, next step is to interpret them fully so that finding can be described in any written form…
The main purpose of this step is to find out whether findings support or contradict with your initial hypothesis. These hypotheses are nothing less than objective of your statistics analysis paper or report (like what you are going to do in your written work).
If you want to compare two or more groups of data, then this process will require some extra tasks like “controlling extraneous variables” in order to ensure that the only difference between these given groups is under analysis i.e., variable/s under study.
After doing all these processes, writer can finally start writing their descriptive statistics paper or report.
What is descriptive statistics?
Descriptive statistics is a collection of tools and techniques used by statisticians and other analysts for analyzing collected as well as gathered statistical information from different sources; it’s also known as mathematical approach for describing the characteristics (like mean, median etc.) about given data set along with graphical representations (like histogram etc.) for clearly presenting those results.
Descriptive statistics is an important step of research because it helps in determining whether data set collected has any statistical patterns, trends etc. which are result of some natural or social phenomenon; if descriptive statistician (who works for the company) finds any such pattern then he will report and write a work about possible reasons behind that pattern i.e., finding; this task can be performed by any number of professionals like statisticians, analysts etc. depending on nature/purpose of their study. Here we will discuss some tasks being perform by generic descriptive statistician while analyzing collected data (gathered evidences).
What should be considered before conducting descriptive statistics analysis?
Once you have finalized your topic and decided to conduct your statistics analysis paper or report, you should think about two things such as:
- objective of descriptive analysis (what is the main purpose of conducting this procedure);
- type of data used for descriptive analysis i.e., what kind of collected information are going to be analyzed using statistical techniques/methods (quantitative or qualitative etc.).
What are the tools most commonly used by descriptive statistician?
As already discussed before that there might be two types of data being analyzed by descriptive statisticians depending upon their topics like first one is quantitative data set and second one is qualitative data set; it’s important to perform different procedures while analyzing these given sets so here we will discuss few most frequently used tools/techniques about each type of data i.e., quantitative and qualitative.
How to analyze Quantitative Data?
As already discussed before that there are several procedures for analyzing collected and gathered quantitative information based on your research objectives; but there is one general rule which will ensure the quality of analysis being done (researchers should follow this rule always while performing purposeful descriptive statistics paper or report) like first thing researcher must pay attention towards is “covered samples” i.e., how many number of interested individuals participated in their study so…
What is Quantitative Data?
Number of things can be categorized into quantitative data means all the information which can be measure or expressed using specific numbers e.g., how many citizens used internet in given country; so this information must include: 1) countable numbers (which are called whole numbers aka integers); 2) natural numbers (which are also known as counting number); 3) rational numbers (meaningless without a fractional component, like halves, fourths and etc.). Common examples of quantitative data sets include salary, length, weight etc. which have some measurement attached with them while analyzing these collected evidences… So now let’s discuss about few most frequently used tools/techniques about each type of data i.e., qualitative and quantitative.
How to analyze Qualitative Data?
There are many procedures for analyzing collected and gathered qualitative information based on your research objectives; but there is one general rule which will ensure the quality of analysis being done like first thing researcher must pay attention towards is “covered samples” i.e., how many number of interested individuals participated in their study so…
What is Qualitative Data?
Qualitative data usually refers to those types of information that does not have any value attached with it e.g., color, shape etc.; usually considered as parts or whole e.g., letters are part of alphabet while language English is a whole i.e., collection of all alphabets (consisting both lowercase and uppercase letters). Common examples of qualitative data set include alphabets, colors etc. which have some certain meaning attached with them while analyzing these collected evidences…
Statistics analysis paper writing tips
- Make sure your statistical analysis paper is well-written, and has proper grammar.
- Be creative with word choice! You may also choose to add quotes from the literature that support your hypotheses and claims.
- Using quotes from reputable sources will strengthen the impact of your work. Remember, one must cite their work when using information taken from another source (i.e., a book/journal article).
- When referencing a quote, make sure you include page numbers!
- Your paper should be a minimum of 3 pages long.
- A statistics analysis research paper should have an intro section which includes problem statement and research question; methods used; hypothesis development; results conclusion; discussion section regarding finding relevance to real life problems, any limitations of study, future studies that could be done; reference list should be at end.
- Avoid grammar mistakes when writing your statistics essay!
Note : Include your name, course number and lecturer’s name on the top right hand corner of the first page of your statistical analysis paper.
Tips on Writing a Statistical Analysis Essay
- Ensure you interpret statistical data correctly
- Support calculation of the statistics with the procedure used to reach the final figures
- Include visuals such as tables and graphs in your essay.
- Avoid grammatical and spelling errors in your essay.
- If necessary, include as much information as possible to enable the reader to interpret and reconstruct your argument.
Writing a statistical analysis essay can be challenging without the right format. We hope you get it right after going through the outlined process. Nonetheless, if you find the writing too demanding or overwhelming, then you can gladly request us for assistance.
5 steps if writing a good statistical analysis essay
Following steps will guide you through the process of writing a good statistics analysis paper:
Step 1 : Exploring your topic and gathering information .
Step 2 : Making outline for statistical analysis paper (or report).
Step 3 : Drafting the paper using different paragraphs i.e., body paragraphs; each one describes a part of the research i.e., data analysis process
Step 4 : Editing and proofreading your draft.
Step 5 : Make sure you have included the correct referencing style/methods for writing your paper (or report)
Statistical Analysis Paper Example
Title : Statistical Analysis in Psychology – A Case Study
Authors’ names and affiliation : Student Name, University of New York State School of Psychology
Abstract or Summary : Social psychologists have studied aggression for decades, yet results are still misinterpreted by the public and the media. In fact, social psychologists themselves continue to debate key issues such as whether aggressive behavior is caused by circumstances or personality traits. This paper aims to analyze a case study of violent criminal acts committed in 2004 , with particular emphasis on how aggression is manifested in individuals who commit such crimes. We begin by making observations based on our research findings from past studies of violent crime. Using these results, we hypothesize that violence has more to do with environmental factors than innate biological dispositions. Next, we take a look at the environment where this robbery took place (e.g., physical setting), again using data from our research base for further analysis. Then, we describe ways in which schools can implement effective methods for reducing the risk of aggression occurring on school premises. This paper will not only call attention to the shortcomings of past research in this area, but also make recommendations for further studies .
Finally, we conclude by thanking our readers for their interest in psychology as a science and encourage them to review other works in the field (e.g., books, journals), which provide useful information and help individuals lead happier lives.
Main body : With crime rates increasing every year all over the world , it is essential that social psychologists study aggressive human behavior from scientific perspectives so as to develop effective prevention strategies at both individual and community levels . The term aggression can be defined as any kind of hostile behavior with the aim of hurting another person or depriving him/her of a desired object, whereas violence refers to the use of physical force against another person with the intention of causing severe harm. In this paper, we present the findings from our investigation into violent crime at College Avenue, New York City (A case study). Our goal is twofold: firstly, and most importantly, we aim to determine whether aggression is innate in criminals; secondly, we hope to determine why crimes like these continue to occur in such large numbers.
About Author : John is a third year Psychology student at the University of New York State. He is particularly interested in studying human aggression and its causes. The purpose of this paper is twofold: firstly, and most importantly, he aims to determine whether aggression is innate in criminals; secondly, he hopes to determine why crimes like these continue to occur in such large numbers.
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In most instances, data collection on an entire population is delicate (Chance, 2002). I got the necessary skills in sampling techniques in this
What I Have LearnedUsing the space below, write a reflective essay about your learning experience onusing statistical techniques in data analysis.
I have skills to analyze data from a sample and make conclusions regarding the entire population by using statistical probabilities and test of
Reflective essay about your learning experience on using statistical techniques in data analysis - 11947352.
Overall, this project has been a good learning experience for me. I become more knowledgeable towards satistics and it will definetly aid me in future research
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Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
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