sample percentiles

简明释义

样本百分位数

英英释义

例句

1.To assess the effectiveness of the new drug, researchers examined the sample percentiles 样本百分位数 of patient recovery times.

为了评估新药的有效性，研究人员检查了患者恢复时间的样本百分位数 样本百分位数。

2.The sample percentiles 样本百分位数 help identify outliers in the data set.

在数据集中，样本百分位数 样本百分位数帮助识别异常值。

3.When analyzing survey results, calculating the sample percentiles 样本百分位数 can provide insights into respondent behavior.

在分析调查结果时，计算样本百分位数 样本百分位数可以提供对受访者行为的见解。

4.The report includes the sample percentiles 样本百分位数 for income levels across different demographics.

该报告包括不同人口统计中收入水平的样本百分位数 样本百分位数。

5.In our study, we calculated the sample percentiles 样本百分位数 to understand the distribution of test scores among students.

在我们的研究中，我们计算了样本百分位数 样本百分位数以了解学生考试成绩的分布。

作文

Understanding statistical concepts is essential in various fields, including business, healthcare, and social sciences. One important concept that often comes into play is the idea of sample percentiles (样本百分位数). Sample percentiles are used to interpret and analyze data by indicating the relative standing of a data point within a dataset. In simple terms, they help us understand how a particular value compares to others in the sample. For instance, if we say that a student's test score is in the 90th percentile, it means that this student scored better than 90% of their peers. This kind of information can be crucial for making decisions based on data.To illustrate the importance of sample percentiles (样本百分位数), consider a scenario where a teacher wants to evaluate the performance of students in a class. By calculating the percentiles of the test scores, the teacher can identify which students are performing exceptionally well and which ones may need additional support. For example, if the lowest score in the class is at the 10th percentile, it indicates that 10% of the students scored lower than this particular score. Conversely, if a score falls in the 75th percentile, it suggests that the student has performed better than 75% of the class.In research, sample percentiles (样本百分位数) play a critical role in summarizing large datasets. Researchers often deal with vast amounts of data, and percentiles provide a simple way to convey complex information. For example, in a medical study analyzing the effects of a new drug, researchers might report the 25th, 50th, and 75th percentiles of patient recovery times. This allows stakeholders to quickly grasp the range of recovery times and understand how the majority of patients are responding to the treatment.Moreover, sample percentiles (样本百分位数) can also be used for benchmarking purposes. Businesses frequently use percentiles to compare their performance against industry standards. For instance, if a company finds that its customer satisfaction score is in the 80th percentile compared to competitors, it indicates that they are performing better than most in the industry. This knowledge can guide strategic decisions, such as whether to invest in further improving customer service or to focus on other areas.However, it is essential to note that while sample percentiles (样本百分位数) provide valuable insights, they should not be the sole basis for decision-making. Percentiles can sometimes be misleading if the underlying data distribution is skewed. For example, in a dataset where a few extremely high values exist, the median (the 50th percentile) may not accurately represent the typical experience of the majority. Therefore, it is crucial to consider other statistical measures alongside percentiles, such as mean, standard deviation, and the overall distribution shape.In conclusion, sample percentiles (样本百分位数) are a powerful tool for data analysis and interpretation. They provide a clear framework for understanding the relative standing of individual data points within a dataset. Whether in education, research, or business, percentiles help stakeholders make informed decisions based on data. However, like any statistical measure, they should be used judiciously and in conjunction with other analytical tools to ensure a comprehensive understanding of the data at hand.