experimental data

简明释义

试验数据

英英释义

例句

1.The experimental data showed a clear correlation between temperature and reaction rate.

这份实验数据显示温度与反应速率之间有明显的相关性。

2.The findings were published in a journal along with the experimental data supporting their claims.

研究结果连同支持其主张的实验数据一起发表在期刊上。

3.After reviewing the experimental data, the team decided to modify their approach.

在审查了实验数据后，团队决定修改他们的方法。

4.The researchers analyzed the experimental data to draw significant conclusions about the new drug's efficacy.

研究人员分析了实验数据以得出关于新药疗效的重要结论。

5.In order to validate their hypothesis, the scientists collected experimental data from multiple trials.

为了验证他们的假设，科学家们从多个试验中收集了实验数据。

作文

In the realm of scientific research, the term experimental data refers to the information collected through observation and experimentation. This data is crucial for validating hypotheses and theories in various fields, including physics, chemistry, biology, and social sciences. The process of gathering experimental data often involves meticulous planning, execution, and analysis to ensure accuracy and reliability. To begin with, the collection of experimental data typically starts with a well-defined hypothesis. Researchers formulate a hypothesis based on existing knowledge or observations, which they then test through experiments. For instance, a biologist might hypothesize that a certain fertilizer improves plant growth. To test this, they would set up an experiment where one group of plants receives the fertilizer while another group does not. The differences in growth between these two groups will provide the experimental data needed to support or refute the hypothesis.Once the experiment is conducted, researchers collect experimental data systematically. This may include quantitative measurements, such as the height of the plants, the number of leaves, or even biochemical markers. In some cases, qualitative data might also be gathered, such as observations about the color or health of the plants. Regardless of the type, all experimental data must be recorded accurately to ensure that the findings are credible.After data collection, the next step involves analyzing the experimental data. This analysis can take many forms, from simple statistical calculations to complex modeling. Researchers often use software tools to help interpret their data, looking for patterns or correlations that can provide insights into their hypothesis. For example, if the biologist finds that the plants receiving fertilizer consistently grow taller than those that do not, this could indicate that the fertilizer is effective, supported by the experimental data collected.However, it is essential to approach experimental data with caution. Researchers must consider potential sources of error or bias that could affect the results. For instance, environmental factors such as light, water, and soil conditions can vary significantly and influence plant growth. Therefore, controlling these variables is critical in ensuring that the experimental data reflects the true effects of the fertilizer. Additionally, replicating experiments and peer review processes help validate the findings and strengthen the conclusions drawn from the experimental data.In conclusion, experimental data is a fundamental aspect of scientific inquiry. It serves as the backbone for testing hypotheses and advancing knowledge across various disciplines. By carefully collecting, analyzing, and interpreting experimental data, researchers can draw meaningful conclusions that contribute to our understanding of the world. As science continues to evolve, the importance of robust and reliable experimental data will remain paramount in driving innovation and discovery. The significance of experimental data extends beyond academia; it influences policy-making, technological advancements, and everyday life decisions. For instance, clinical trials in medicine rely heavily on experimental data to determine the efficacy and safety of new drugs before they reach the market. Similarly, environmental studies depend on experimental data to assess the impact of human activities on ecosystems. Thus, mastering the concept of experimental data is essential not only for scientists but for anyone engaged in informed decision-making based on evidence.

在科学研究领域，术语实验数据指的是通过观察和实验收集的信息。这些数据对于验证各种领域的假设和理论至关重要，包括物理学、化学、生物学和社会科学。收集实验数据的过程通常涉及细致的规划、执行和分析，以确保准确性和可靠性。首先，实验数据的收集通常始于明确的假设。研究人员根据现有知识或观察制定假设，然后通过实验进行检验。例如，一位生物学家可能假设某种肥料可以改善植物生长。为了验证这一点，他们会设置一个实验，其中一组植物施用肥料，而另一组则不施用。两个组之间的生长差异将提供支持或反驳假设所需的实验数据。一旦实验进行，研究人员就会系统地收集实验数据。这可能包括定量测量，例如植物的高度、叶子的数量，甚至生化标记。在某些情况下，可能还会收集定性数据，例如关于植物颜色或健康状况的观察。无论类型如何，所有实验数据都必须准确记录，以确保研究结果的可信性。数据收集后，下一步是分析实验数据。这种分析可以采取多种形式，从简单的统计计算到复杂的建模。研究人员通常使用软件工具来帮助解释他们的数据，寻找可能提供对假设见解的模式或相关性。例如，如果生物学家发现施用肥料的植物比未施用肥料的植物持续长得更高，这可能表明肥料有效，并得到收集的实验数据的支持。然而，必须谨慎对待实验数据。研究人员必须考虑可能影响结果的误差或偏见来源。例如，光照、水分和土壤条件等环境因素可能变化很大，并影响植物生长。因此，控制这些变量对于确保实验数据反映肥料的真实效果至关重要。此外，重复实验和同行评审过程有助于验证研究结果并增强从实验数据中得出的结论。总之，实验数据是科学探究的基本方面。它作为测试假设和推动各学科知识进步的支柱。通过仔细收集、分析和解释实验数据，研究人员可以得出有意义的结论，为我们理解世界做出贡献。随着科学的不断发展，可靠的实验数据的重要性在推动创新和发现中将始终占据重要地位。实验数据的重要性超越了学术界；它影响政策制定、技术进步和日常生活决策。例如，医学中的临床试验在上市之前严重依赖实验数据来确定新药的有效性和安全性。同样，环境研究依赖于实验数据来评估人类活动对生态系统的影响。因此，掌握实验数据的概念对于科学家和任何参与基于证据的知情决策的人来说都是至关重要的。