Statistical Inference
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Statistical inference is a critical aspect of research, allowing researchers to draw conclusions about a population based on a sample. In the SOSC341 Final Exam 2022-2023, you may encounter questions related to statistical inference. 

In this blog post, we will provide you with some questions and answers related to statistical inference that may help you prepare for the exam.

What is Statistical Inference?

Statistical inference is the process of using a sample to make conclusions about a larger population. It involves making estimates and testing hypotheses about population parameters, such as means and proportions, based on sample statistics.

Table: Common Statistical Inference Terminologies

PopulationThe entire group of individuals or objects that researchers are interested in studying.
Samplea subset of the population that is selected for study.
Parametera numerical value that describes a characteristic of a population.
Statistica numerical value that describes a characteristic of a sample.
Confidence Intervala range of values that is likely to contain the population parameter with a certain level of confidence.
Hypothesis TestA statistical test is used to determine whether an observed effect or relationship is likely to have occurred by chance or is a true effect or relationship.

What is the Central Limit Theorem?

The Central Limit Theorem is a fundamental concept in statistical inference. It states that as the sample size increases, the distribution of sample means approaches a normal distribution, regardless of the shape of the population distribution. This means that even if the population is not normally distributed, the distribution of sample means will be approximately normal if the sample size is sufficiently large.

Table: Steps Involved in Hypothesis Testing

Step 1: State the Null and Alternative HypothesesThe null hypothesis is the default assumption that there is no difference or no effect in the population, while the alternative hypothesis is the opposite of the null hypothesis.
Step 2: Choose a Significance LevelThe significance level is the maximum probability of making a Type I error, which is rejecting the null hypothesis when it is true. Commonly used significance levels are 0.05 and 0.01.
Step 3: Calculate the Test StatisticThe test statistic is a numerical value that summarizes the difference between the sample statistic and the hypothesized population parameter under the null hypothesis.
Step 4: Determine the Critical ValueThe critical value is the value beyond which the null hypothesis is rejected. It is based on the significance level and the degrees of freedom.
Step 5: Make a DecisionIf the test statistic falls beyond the critical value, the null hypothesis is rejected. Otherwise, the null hypothesis is not rejected.
Step 6: Interpret the ResultsIf the null hypothesis is rejected, it means that there is evidence to support the alternative hypothesis. If the null hypothesis is not rejected, it means that there is not enough evidence to support the alternative hypothesis.


Statistical inference is a complex topic, but it is essential for making informed conclusions in research. By understanding the terminology, concepts, and procedures of statistical inference, you can effectively analyze and interpret data. We hope this blog post has provided you with some useful information to help you prepare for the SOSC341 Final Exam 2023

Remember to practice and review the concepts regularly to reinforce your understanding. Additionally, seek out resources such as textbooks, online tutorials, and practice problems to further your knowledge. With dedication and hard work, you can master statistical inference and apply it to your research and everyday life. Good luck on your exam!

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