Next, we need a list of random numbers before we can select the sample of 200 students from the total list of 10,000 students. In our case, this would mean assigning a consecutive number from 1 to 10,000 (i.e., N = 10,000 the population of students at the university). We now need to assign a consecutive number from 1 to N, next to each of the students. You can read about this later in the article under Disadvantages of simple random sampling. ![]() If you were actually carrying out this research, you would most likely have had to receive permission from Student Records (or another department in the university) to view a list of all students studying at the university. To select a sample of 200 students, we need to identify all 10,000 students at the university. This may have suggested that we needed a larger sample size perhaps as many as 400 students. However, we could have also determined the sample size we needed using a sample size calculation, which is a particularly useful statistical tool. This number was chosen because it reflects the limit of our budget and the time we have to distribute our questionnaire to students. Let's imagine that we choose a sample size of 200 students. If we were only interested in female university students, for example, we would exclude all males in creating our sampling frame, which would be much less than 10,000 students. Since we are interested in all of these university students, we can say that our sampling frame is all 10,000 students. It is also important to consider the context and purpose of the analysis when selecting bin sizes and locations, as different approaches may be more appropriate for different types of data and research questions.In our example, the population is the 10,000 students at the single university. For example, using smaller bins can provide more detail about the distribution, while using larger bins can highlight broader trends or patterns. One way to address this problem is to use different bin sizes and locations to explore different aspects of the data. How to address the problem that a histogram depends on the number and location of the bins: It may not be as effective at highlighting patterns or trends in the data, such as gaps or clusters.Ĥ. A stem and leaf plot may not be as visually appealing or intuitive as a histogram, especially for larger datasets. ![]() It can be easier to read and interpret than a histogram, especially for smaller datasets. A stem and leaf plot can provide more detailed information about the distribution of the data, including the exact values of each data point. Advantage and disadvantage of a stem and leaf plot with respect to a standard histogram: However, it may not be appropriate for all types of data, such as when the population is not well-defined or when there are specific subgroups that need to be represented in the sample.ģ. This reduces the risk of sampling bias and ensures that the sample is representative of the population. Simple random sampling is a good approach to sampling because it ensures that every member of the population has an equal chance of being selected for the sample. Simple random sampling (without replacement) as a good approach to sampling: The size and composition of the sample can affect the results, making it difficult to compare results across different samples.Ģ. Sampling may not capture all the important information in the dataset, leading to incomplete or inaccurate conclusions. There is a risk of sampling bias, where the sample may not accurately represent the population. Can provide a representative sample of the population, allowing for generalizations to be made about the entire population. Can save time and resources, especially when dealing with large datasets. ![]() Reduces the amount of data that needs to be processed and displayed, making it easier to analyze and interpret. Advantages and disadvantages of using sampling to reduce the number of data objects that need to be displayed:
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