Descriptive and Inferential Statistics

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Week Seven Homework Exercise Answer the following questions, covering material from Ch. 13 of Methods in Behavioral Research Define inferential statistics and how researchers use inferential statistics to draw conclusions from sample data. According to Cozby (2009) inferential statistics are used to determine whether we can in fact make statements that the results reflect what would happen if we were to conduct the experiment again and again with multiple samples Define probability and discuss how it relates to the concept of statistical significance. Probability is the possible that an outcome of an experience or an event will occur (Cozby 2009) Statistical significant and probability are one in the same. A researcher is studying the

The quantitative research is also based on statistical reports on correlations, regression analysis, comparisons of means and variances, and statistical significance of findings.

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

The main purpose of the most researchers in conducting a research study is to achieve a statistically significant result. When we say statistically significant, it means that the result in a research study was not attributed to chance. In addition, it also means

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

When analyzing data, such as the marks achieved by 100 students for a piece of coursework, it is possible to use both descriptive and inferential statistics in your analysis of their marks. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyses your results and draw conclusions. So what are descriptive and inferential statistics? And what are their differences? Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analyzed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

2. Inferential statistics refers to generalizing from a sample to a population, estimating unknown parameters, drawing conclusions, and making decisions.

Quantitative data analysis is the process of presenting and interpreting numerical data using descriptive statistics and inferential statistics. The descriptive statistics include both measures of central tendency and measures of variability. Measure of central tendency includes mean, median and mode while measures of viability identify the average, which include range and standard deviation. Inferential statistics provide the outcome of statistical tests that assists a deduction from data collected. Since quantitative research collects data through survey, a protection of human participants is very critical to enhance ethical protocol in the research process. (Weisman, n.d.).

The statistical descriptive strategy will be correlation, which is defined as “two different variables are observed to determine whether there is a relationship between them” (Gravetter & Wallnau, p. 11, 2009). The descriptive statistic will be presented as percentages, mode and mean. The inferential data will be conducted using a chi-square test for independence. According to Gravetter and Wallnau’s definition of the Chi-square test for independence states that it “uses the frequency data from a sample to evaluate the relationship between variables in the population” (2009, p.

Thus, the choice by Richmond, Merrick, Green, Dinh, & Iedema (2011) to report p-values and confidence values (CI) in establishing the significance of the results assists in enhancing the reliability and validity of the study. Whereas, Tobiano, Chaboyer, & Mcmurray (2012) adopted thematic analysis in data analysis, which facilitated flexibility while maintaining research rigor. The straightforward nature of the thematic analysis also contributes in enhancing the validity of the provided evidence. In the case of Sand-Jecklin & Sherman (2014), the presentation of the findings in terms of means, standard deviation (SD), p-values and degree of freedom (df), coupled with the use of tables in presentation facilitated easier understanding of the values and identification of any trend likely to emerge. The presentation was critical in improving the validity of the findings

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

Two important factors of psychology and studying it are statistics and research. This paper will explain what research is and the scientific method. This paper will also explain how research is important to psychology. When a researcher does studies on psychology they receive data from the subjects, they are studying. In any type of research there are two major types of data, they are primary data and secondary data. Data is important to statistics because it is the information a researcher receives from the studies they perform. Statistics puts all the data into a form that many people can understand. Statistics is an excellent way to

This non-experimental research uses descriptive statistical analysis, paired t-tests, and analysis of variance to examine the study variables.

Major League Baseball is known as America’s favorite pastime, and MLB teams spend an extensive amount of money in the excess of a billion dollars with the ultimate goal to win the World Series. This learning team’s focus throughout this descriptive statistics paper is the MLB players’ performances, salaries, salary caps, and winning percentages. Though salaries will by no means be a trade for wins, the goal is to use the less experienced players and pay them a lower salary. Research has been done on whether or not player’s salaries and wins are connected.