SSC CGL Exam > SSC CGL Videos > General Intelligence and Reasoning for SSC CGL > Introduction: Inference
Introduction: Inference Video Lecture | General Intelligence and Reasoning for SSC CGL
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FAQs on Introduction: Inference Video Lecture - General Intelligence and Reasoning for SSC CGL
| 1. What is the importance of inference in statistical analysis? |  |
Ans. Inference is crucial in statistical analysis as it allows us to draw conclusions and make predictions about a population based on a sample. It helps us understand the relationship between variables, test hypotheses, and make informed decisions.
| 2. How is inference different from descriptive statistics? | |
Ans. Descriptive statistics summarize and describe the characteristics of a dataset, while inference involves making conclusions and predictions about a population based on the data collected from a sample. Descriptive statistics provide a snapshot of the data, while inference allows us to generalize the findings to the entire population.
| 3. What are the two types of inference in statistics? | |
Ans. The two types of inference in statistics are hypothesis testing and estimation. Hypothesis testing involves making decisions about a population based on sample data, while estimation involves estimating population parameters based on sample statistics.
| 4. What is the central limit theorem and its role in statistical inference? | |
Ans. The central limit theorem states that when a sample size is sufficiently large, the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution. This theorem is crucial in statistical inference as it allows us to use the normal distribution for making inferences about the population mean.
| 5. What are some common challenges in statistical inference? | |
Ans. Some common challenges in statistical inference include biased sampling, nonresponse bias, confounding variables, and small sample sizes. Biased sampling can lead to inaccurate inferences, nonresponse bias occurs when certain individuals don't respond to a survey, confounding variables can affect the relationship between variables, and small sample sizes may not accurately represent the population.
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Oct 30, 2024 Last updated |
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