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Data Analysis: Analyzing Data - Inferential Statistics

Inferential statistics deal with drawing conclusions and, in some cases, making predictions about the properties of a population based on information obtained from a sample. While descriptive statistics provide information about the central tendency, dispersion, skew, and kurtosis of data, inferential statistics allow making broader statements about the relationships between data. Inferential statistics are frequently used to answer cause-and-effect questions and make predictions. They are also used to investigate differences between and among groups. However, one must understand that inferential statistics by themselves do not prove causality. Such proof is always a function of a given theory, and it is vital that such theory be clearly stated prior to using inferential statistics. Otherwise, their use is little more than a fishing expedition. For example, suppose that statistical methods suggest that on average, men are paid significantly more than women for full-time work. Several competing explanations may exist for this discrepancy. Inferential statistics can provide evidence to prove one theory more accurate than the other. However, any ultimate conclusions about actual causality must come from a theory supported by both the data and sound logic.

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The following briefly introduces some common techniques of inferential statistics and is intended as a guide for determining when certain techniques may be appropriate. The techniques used generally depend on the kinds of variables involved, i.e. nominal, ordinal, or interval. For further information on and/or assistance with a given technique, refer to the books and in-house support listed in the Resources section at the end of this module. + Chi-square (2 2) tests are used to identify…...

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...Descriptive and Inferential Statistics Statistics are all-around and used in everyday life. Statistics are used to describe how effective a medication is for a certain disorder to what the most popular color is in the United States. According to Aron, Aron, and Coups, 2009, “statistics is a method of pursuing truth. As a minimum, statistics can tell you the likelihood that your hunch is true in this time and place and with these sorts of people” (p. 2). Psychologist use two branches of statistics to summarize his or her results and those are descriptive and inferential statistics. This paper will discuss the function of statistics, what descriptive and inferential statistics are, and the relationship between descriptive and inferential statistics. Statistics are used in almost every branch of study and is found behind the scenes in many of our normal daily activities. From economic to scientific studies, statistics are utilized in one way or another. Statistics are an essential part of understanding information and expanding knowledge base and is encompasses almost all aspects of enquiry ("7 Most Essential Functions Of Statistics", 2012). Statistics have many functions that we utilize;statisticsprovides for a better understanding of phenomenon of nature and helps in proper and statistical planning in all forms of study. Using statistics helps in collecting useful quantitative data and also aids in presenting difficult or confusing data in an understandable way. Statistics......

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...Descriptive and Inferential Statistics PSY/315 Statistical Reasoning in Psychology September 21, 2013 Dr. Nancy Walker Descriptive and Inferential Statistics Statistics is “a branch of mathematics that focuses on the organization, analysis, and interpretation of a group of numbers” (Aron, Aron, & Coups, 2009, p. 2). However, just the mention of statistics makes people nervous, although when properly understood, many of the questions statistics tries to answer are very provocative and challenging. Statistics are a collection of information and, data that helps test the theory something is happening or will happen again. The functions of statistics are there to help researchers have a better understanding of a phenomenon. Statistics can be used when looking for the truth, if you have ever had a hunch about something, was it confirmed? Yes the hunch was confirmed. Statistics help researchers with data by using math and working with a group of numbers. Statistics studies variables, characteristics that have different values, values, possible number that a score can have, and score one person value of a variable (Aron, Aron, & Coups, 2009). Descriptive and inferential statistics are to evaluate results and enable one to make a conclusion. Descriptive statistics are a way to describe data (Laird Statistics, 2013), as well as to “summarize and describe a group of numbers from a research study,” whereas, inferential statistics are used to “draw conclusions......

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...Descriptive and Inferential Statistics Presentation Tony Roberson, Amani Wilson, Deandra Cobb, and Lysa Satterwhite PSY 315 November 11, 2013 Melinda Waife Descriptive and Inferential Statistics Presentation Click on link below to review Team D’s presentation. http://prezi.com/sz-i9ukpbarl/?utm_campaign=share&utm_medium=copy Tony’s Presentation Speaker Notes: Introduction: Please review Prezi Source: Flickr User "unity_creative" To understand the simple difference between descriptive and inferential statistics, all you need to remember is that descriptive statistics summarize your current dataset and inferential statistics aim to draw conclusions about an additional population outside of proposed data (eCaro, 2003). Deandra Statistics in Psychology and its function cannot be taken lightly. The importance ofthe development of psychology would not have been realized if statistics did not play such a crucial role. Important components such as inferential statistics and interactions are dynamic in the study of associations, and affiliations that are essential in psychology.Statistic is the exact phenomenon of nature and it helps in providing a better understanding. Statistics helps in the effectiveness and planning of statistical analysis in any field of study. Furthermore, helps in applicable quantitative data and in presenting complex data in a suitable level, diagrammatic and graphic form for a clear comprehension of the data. Amani Wilson Speaker......

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...Descriptive and Inferential Statistics Paper Casie Thibeault PSY/315 July 27, 2013 Michelle A. Williams, PhD Descriptive and Inferential Statistics Paper The very word “statistics” seems to produce anxiety in most students - anxiety produced from its connection to mathematics. The first step in controlling anxiety is to understand the connection and just how useful statistics can be for comprehending information that has been gathered. A statistic is a representation of information, and its function is to help researchers either to organize, summarize, or understand data. The ability to describe data is essential when gathering statistics. Statistics can be broken down into two basic types: descriptive statistics and inferential statistics. Descriptive statistics are a summary of information that makes the data presented more easily understood. The descriptive method is limited to only the population in which the researcher is dealing with, and only describes that particular group (Purdue OWL, 1995-2013). Inferential statistics offers a more detailed conclusion regarding the hypothesis. A benefit of the inferential method is that it can be used to take a broader view of populations, making it possible to draw conclusions about sizeable groups of people (Purdue OWL, 1995-2013). In a nutshell, the simple way to distinguish between the two would be that descriptive statistics summarize and inferential statistics draw conclusions. Both descriptive and......

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...standard deviation and variance. There are two main branches of statistics that include descriptive statistics and inferential statistics.Descriptive statistics gives numerical measures that describes the features of a given set of data. Inferential statistics on the other hand takes a sample of a given population, analyses the sample, and from it draw conclusions about the population .Malcolm.O.Asadoorian and Demetrius Kantarelis in their book: Essentials of inferential statistics argue that descriptive statistics organize , summarize and display data whereas inferential statistics utilize probabilistic techniques to analyze sample information from a certain population to improve our knowledge about the population. Measures of central tendancy and variability fall under descriptive statistics. Inferential statistics is divided into two i.e confidence interval which give a range of values for unknown parameters of a population by measuring a statistical sample and the test of significance also called hypothesis testing whereby a claim about a population is tested by analyzing a statistical sample. Descriptive statistics only gives numerical measures to describe a set of data but we cannot draw conclusions. Inferential statistics on the other hand allows us to draw conclusions from a given set of data. From the above scenio the psychologist will require use of inferential statistics inorder to fully draw conclusions and make assertions. Therefore the psychologist will draw ......

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...Inferential Statistics QNT/561 September 1, 2014 INFERENTIAL STATISTICS SAT scores from 48 students in a low-performing district were analyzed and a descriptive statistical analysis performed. These students were given individualized SAT coaching and their scores after coaching were compared and analyzed against their scores prior to receiving the individualized coaching. Based on the results of that analysis, we now want to determine whether the findings are indicative of the entire population of SAT taking individuals. Hypothesis: Ho=Individualized SAT coaching did not result in an increase in SAT scores. H1=Individualized SAT coaching did result in an increase in SAT scores. Inferential Statistics Distribution: Normal Regression Statistics | Multiple R | 0.985001 | R Square | 0.970227 | Adjusted R Square | 0.96958 | Standard Error | 13.59259 | Observations | 48 | ANOVA | | | | | | | df | SS | MS | F | Significance F | Regression | 1 | 276957.9 | 276957.9 | 1499.026 | 9.38E-37 | Residual | 46 | 8498.895 | 184.7586 | | | Total | 47 | 285456.8 | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | Intercept | -148.751 | 31.54092 | -4.71611 | 2.26E-05 | -212.239 | -85.2619 | -212.239 | -85.2619 | X Variable 1 | 1.133406 | 0.029274 | 38.71726 | 9.38E-37 | 1.074481 | 1.192331 | 1.074481 | 1.192331 | t-Test: Paired Two Sample for Means | | | | |......

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...Descriptive and Inferential Statistics Paper Terrance Douglas, Katie Faiman, Marika Schlindwein, Christyl Schoultz, & Samantha Sisk PSY/315 February 3, 2013 Dr. Deborah Suzzane Descriptive and Inferential Statistics Paper Have you ever noticed that we just keep moving forward? There are countless, unseen individuals who make this happen each day, but how do they operate? How do they accomplish all of this? We live in a complex world. Behind the scenes, researchers are steadily developing new theories and testing their outcome. For them, statistics serves a very different purpose. In the next few paragraphs, the role of statistics is explained as their role in the psychological community. Statistics itself is then further subdivided into two different methodologies; descriptive and the inferential (Aaron & Aaron & Coups, 2009). Each method utilizes data for a different purpose, and in each method, data may be gathered differently. Lastly, an example of each of the two types of statistics which helps the reader to distinguish clearly between the descriptive and inferential types of statistics which researchers use to conduct their work. It will further be shown how the two methods of statistics relate to each other in research. It is by understanding the two different roles of each of these types of statistics that researchers are able to gather meaningful data, which is testable and provable and keeps us on a forward moving......

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...Inferential Stats and Interpretation PhoenixDrone QNT/561 June 15, 2015 Inferential Statistics and Findings The team individually did an excellent job in interpreting the findings from their prospective. What would have been helpful is if we had used the same data sets to reach our conclusion. The team collaboration was completed after some of the individual papers had been completed and therefore the needed data was not used. The team agreed that null hypothesis would be the best approach to reaching a conclusion to answer the RQ. (a) RQ: Is there a relationship between employee satisfaction (IV) and selected shifts and/or patient wait times (DV)? b) Survey: eight questions were asked in the survey, from a population of 500, 386 answered the survey questions (n=386). The first three questions of preferred shifts to work, the majority was satisfied with the shifts they worked. The majority of employees did not admit to sometimes sleeping at work, overwhelmingly they strongly disagreed. The next question indicated there is a strong negative correlation with job satisfaction and appropriate staffing for adequate patient management as well as feeling appreciated at work. The Hypothesis Statement: H0} There is no correlation between employee satisfaction (IV) and selected shifts and/or patient wait times (DV)? H1} There is correlation between employee satisfaction (IV) and......

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...growth rate tells us about the typical performance of a state. Therefore illustrative statistics attempts to catch a sizable group of observations and offers us some concept concerning the data-set. Descriptive statistics aims to describe data set information with summary graphs and tables (Linda Hollis, n.d.). Inferential Statistics Inferential statistics includes drawing the correct conclusions from your statistical evaluation that's been performed using descriptive data. Ultimately, it really is the inferences that make studies significant and this element is dealt with-in inferential data. Most forecasts of the potential and generalizations of a population by analyzing a smaller sample come under the scope of inferential statistics. Many social sciences experiments offer with analyzing a little sample data which helps determine the way the overall population in general acts. By developing the best experiment, the investigator has the capacity to draw conclusions applicable to his research. Inferential statistics, it is testing a hypothesis and drawing conclusions of a population, centered on set samples. Strengths and weaknesses of Descriptive and Inferential Statistics “Descriptive Research” does not rely on just one form of data; it relies on both “quantitative and qualitative” research methods. The research can use strategies including surveys, observation, area experiments, interviews and quantity analysis. As it is indeed diverse in technique and utilization,......

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...Inferential Statistics and Findings QNT/561 July 13, 2015 Inferential Statistics and Findings The purpose of this paper is “to create inferential statistics (hypothesis test) using the research question and two variables (dependent, DV and independent, IV). The paper focuses on the statistical tool used to test the hypotheses at 95% confidence interval and interprets the results providing the findings” (Instructions-week 5, 2014). Oso Inc., produces hand crafted, unique, and different styles of pens. It sells approximately 100,000 pens to distributors and individual customers and the sales report show good sales numbers. But the company wants to determine how they can increase their profitability, thus approached the research team who proposed the following research question. Research question (RQ) Is there a correlation between Product Profitability (DV) and Product sales (IV)? Hypothesis statements (H0): There is no correlation between Product Profitability (DV) and Product sales (IV). (H1): There is a there is a correlation between Product Profitability (DV) and Product sales (IV). Hypothesis test with a 95% confidence level, using the statistical tool (P-value) “The viability of the null hypothesis is determined in light of the data and the aim of tests of significance is to calculate the probability (P value), that an observed outcome has merely happened by chance. Reject the null hypothesis, if there is significant evidence that, ...

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...Descriptive and Inferential Statistical Method RES/351 Researchers have many tools available to them in order test and analyze a specific hypothesis. Some of these tools help gather data, while others help ensure accurate and relevant analysis. Data collection can take the form of quantitative and qualitative methods. In a qualitative method, your data is more interpretive. This data is used when trying to discover more of a meaning of specific question rather than the frequency (Cooper, Schindler 2014). This data is generally obtained through interviews, participant observation, or focus groups. On the other hand quantitative data is the precise measurement of a specific behavior or phenomena (Cooper, Schindler 2014). Quantitative data is generally gathered by experiments, standardized testing, surveys, or non-participant observation (Cooper, Schindler 2014). While gathering both types of data, it is important to focus on the type of sampling method you utilize. For example, simple random sampling, or probability sampling, can be used to test a targeted representation of a test population (Cooper, Schindler 2014). There are other sampling methods as well. Stratified random sampling, for instance, is probability sampling that draws from each strata of population. One study I found to demonstrate descriptive statistics used stratified random statistics. In this study, they used stratified random sampling to test accuracy in medical billing......

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...an analyzer will incorporate both descriptive and inferential statistics to evaluate his or her results and create a credible conclusion. Descriptive statistics provides information focused on an immediate group of data. After defining what needs to be analyzed, the descriptive statistics will help the analyzer abridge the data to a more meaningful and comprehendible form, which will then provide patterns in his or her research that, will provide a foundation to his or her thesis. For example, a person could use descriptive statistics to evaluate the answers on an exam taken by 400 American students, and use descriptive statistics to determine the overall performance of the 400 students at that school. By using descriptive statistics, the analyzer can use his or her findings, to provide useful information regarding which subjects students need to improve most in, and which minority group or grade level are grasping the educational tools provided at the school more effectively, then those not grasping the provided educational tools and still need more room for improvement. While descriptive statistics helps an analyzer assess an immediate group of data from a single population, inferential statistics allow an analyzer to collect data using bits and pieces of samples which are portions of a collection of data focusing on the group or population of interest in which the analyzer research is concentrated on at the time. Inferential statistics will allow the analyzer to create a......

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...8A1 This assignment lets you explore a quasi-experimental model using ANCOVA data analytical approach. By doing this data analysis project, you will understand a new quantitative research model when randomized sampling is not a choice. Specifically, you will develop analytical skills to use covariate to control for or partial out effects of pre-existing differences carried by sampling. To complete the assessment, answer each question, providing IBM SPSS analysis when necessary to support your answer. For this assignment, use the small batch of data provided by Warner's textbook on page 724. These are hypothetical data. We will imagine that a three-group quasi-experimental study was done to compare the effects of three treatments on the aggressive behavior of male children. Xc, the covariate, is a pretest measure of aggressiveness: the number of aggressive behaviors emitted by each child when the child is first placed in a neutral playroom situation. This measure was done prior to exposure to the treatment. Children could not be randomly assigned to treatment groups, so the groups did not start out exactly equivalent on aggressiveness. The dependent variable, Y, is a posttest measure: the number of aggressive behaviors emitted by each child after exposure to one of the three treatments. Treatment A consisted of three different films. The A1 group saw a cartoon animal behaving aggressively. The A2 group saw a human female model behaving aggressively. The A3group saw a human......

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...Advance Inferential Statistics [Name] [Course] [Tutor] [College] [Date] Understand the concepts of Binary Logistic Regression Logistic regression is essential in predicting a categorical variable present in a set of variables that are predictors. During Logistic regression, categorical dependent variable which is the discriminant function is used when all the predictors that are present are continuous and distributed in a nice way. The binary logic regression has become the most preferred data analysis method that describes the relationship between response variable and an explanatory variable that and it is usually used where a variable follows binomial distribution. Assumptions of binary logistic regression Among the assumptions applied in the application of binary logistic regression is that logistic regression usually does not assume a relationship that is linear between the dependable and undependable variables. It is a must for the dependable variable to be a dichotomy i.e. must have two categories. It is also not required that the independent variables to be an interval, distributed in a normal way, linearly related, or even have linear variance within specific groups. Another assumption is that large samples are most important in this regression because it has a maximum likelihood of coefficients of large sample estimates. It is usually recommended that one should have a linear regression of 50 cases and above per predictor...

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...Advance Inferential Statistics [Name] [Course] [Tutor] [College] [Date] Understand the concepts of Binary Logistic Regression Logistic regression is essential in predicting a categorical variable present in a set of variables that are predictors. During Logistic regression, categorical dependent variable which is the discriminant function is used when all the predictors that are present are continuous and distributed in a nice way. The binary logic regression has become the most preferred data analysis method that describes the relationship between response variable and an explanatory variable that and it is usually used where a variable follows binomial distribution. Assumptions of binary logistic regression Among the assumptions applied in the application of binary logistic regression is that logistic regression usually does not assume a relationship that is linear between the dependable and undependable variables. It is a must for the dependable variable to be a dichotomy i.e. must have two categories. It is also not required that the independent variables to be an interval, distributed in a normal way, linearly related, or even have linear variance within specific groups. Another assumption is that large samples are most important in this regression because it has a maximum likelihood of coefficients of large sample estimates. It is usually recommended that one should have a linear regression of 50 cases and above per predictor...

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