Statistic is divided by two methods, there are descriptives statistic and inference statistic. In here, descriptive statistic, we learn about how to describe or display collection data by using table, graphic and diagram. We make pie chart, frequency table, stem and leave diagram, histogram, ogive, etc. Descriptive statistic is statistical methods that summarize and describe a collection of data without general conclusion about population. And, inference statistic is statistical methods that give conclusion about population, in here we use hypothesis statistic, alpha (significance level), degree of freedom, etc.
In thesis or journal or research for quantitative, we often use this two methods, descriptive to describe our collection data, may about respondent like gender, education, or some specific of respondent. And, for inference method we want to make conclusion about population from sample that we are taken.
Inference methods, it divides by parametric and nonparametric statistic.
What are they difference?
The difference between parametric and nonparametric are data characteristic. Parametric, data collection must have the normal distribution, it has interval or ratio scale. But for Nonparametric is not like that. Nonparametric statistic methods, collection data has free distribution (not the normal distribution) and it has nominal or ordinal scale. Parametric method is more complicated than nonparametric .
Parametric method, we need to fulfil the assumption of data for the main test, such as regression, correlation, difference mean test (t-test), etc. There are classical assumption such as normality data, heterocesdastisity, independence error,etc. If we cannot fulfil this assumption, we can do more alternative method such as transformation of collection data or we must change to use nonparametric method.
In research, we focus to want to know about description, association, and comparison of one, two or more variables. And we have more statistical test to know about it.
For example of description are Binomial test, Run Test, t-test, etc. And, the association are Regression Linear, Regression Non Linear, Pearson Correlation, Spearman Correlation, Structural Equation Model, Path analysis, etc. The comparison : ANOVA (one way or two way), Kolmogorov Smirnov, Median Test, t-test independent, t-test paired sample, etc.
And to do that analyze we can do manual by using formula of statistic or use statistical software. For efficient and accurate result, we use statistical software. If we have more data, we need more time and more analyze to do it. And, maybe our analyze will not correct or bias or more error if we use manual analyze.
We can get data from observation, interview or surveys. We can get quantitative and qualitative data. Quantitative data is numeric data, but qualitative is nonnumeric data.
When I choose statistic in bachelor degree as my subject, the reason was not because I like statistic, but I hated statistic. When I was in Mathematical Department to study, I like pure mathematic that study more about Calculus, Algebraa, Analysis, etc. Statistic made me curious and I want to know why I didn't like it. But Alhamdulillah, I can pass it with good grade.
My last thesis in bachelor degree was about Customer Satisfaction in Government Company. I used combine of three analyses, there was factors analysis to know what factor influence customer satisfaction, discriminant analysis to know how was different of customer satisfaction in each area and the last is Parasuraman theory about Gap Analysis to know what percent of customer satisfaction about its service.
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