1) What is Statistical Inference? - Statistical inference is the process of analyzing the result and making conclusions from data subject to random variation. It is also called inferential statistics. Hypothesis testing and confidence intervals are the applications of the statistical inference. Statistical inference is a method of making decisions about the parameters of a population, based on random sampling. 2) What are the components used for making statistical inference and explain? - - - Sample Size Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions. Variability in the sample Sampling variability is how much an estimate varies between samples. “Variability” is another name for range; Variability between samples indicates the range of values differs between samples. Size of the observed differences It is important to have a larger sample size so that the observations can be made considering most of the diversities that exist in a population. By considering a large population, the observation results are most likely to have higher credibility. Once a market researcher collects data from the sample, the process of analysis begins and observed results are attained. 3) What are the different types of statistical inference? - One sample hypothesis testing Confidence Interval Pearson Correlation Bi-variate regression Multi-variate regression Chi-square statistics and contingency table ANOVA or T-test 4) List the procedure involved in inferential statistics. - Begin with a theory. Create a research hypothesis. Operationalize the variables. Recognize the population to which the study results should apply. Formulate a null hypothesis for this population. Accumulate a sample from the population and continue the study. - Conduct statistical tests to see if the collected sample properties are adequately different from what would be expected under the null hypothesis to be able to reject the null hypothesis. 5) Discuss the importance of statistical inference. - It helps to assess the relationship between the dependent and independent variables. The purpose of statistical inference to estimate the uncertainty or sample to sample variation. It allows us to provide a probable range of values for the true values of something in the population. REFENCES: . A. (2021a, March 22). General Data Protection Regulation(GDPR) Guidelines BYJU’S. BYJUS. https://byjus.com/maths/statistical-inference/ . A. (2021b, March 22). General Data Protection Regulation(GDPR) Guidelines BYJU’S. BYJUS. https://byjus.com/maths/statistical-inference/ . S. (2021c, January 1). Sampling Variability: Definition. Statistics How To. https://www.statisticshowto.com/sampling-variability/ Infect Dis, J. (1998). Statistical Inference (part 1): Basic Concepts. pubmed.ncbi.nlm.nih.gov. Retrieved February 2, 2023, from https://pubmed.ncbi.nlm.nih.gov/11101912/#:~:text=Statistical%20inference%20compris es%20the%20application,included%20in%20a%20specific%20sample. Sample size and power. (n.d.). https://www.iwh.on.ca/what-researchers-mean-by/sample-sizeand-power Surendran, A. (2018, July 6). Quantitative Observation: Definition, Characteristics and Examples. QuestionPro. https://www.questionpro.com/blog/quantitative-observation/
