Final answer:
The statement that data preparation involves taking the initial dataset and refining it to a more relevant, smaller set of necessary data is true. This process includes cleaning, transforming, and reducing the data to facilitate more accurate analysis. Proper data preparation is critical for both descriptive and inferential statistics.
Step-by-step explanation:
Data preparation involves taking your initial dataset and refining it down to a smaller, more relevant set of necessary data for analysis. The statement that data preparation involves reducing the dataset to only the necessary data is true. In the process of data science and statistics, raw data is initially collected through various means such as observations or experiments. This data may include irrelevant or extraneous information, which does not contribute to answering the specific question at hand.
During data preparation, scientists conduct data cleaning to remove or correct erroneous data. They also perform data transformation, where data is organized or converted into a suitable format for analysis, such as calculating the averages or sorting data into categories. Additionally, data reduction techniques are applied to simplify the data without losing important information, focusing on key variables and records that are most relevant to the hypotheses being tested.
Moreover, biases can be introduced if relevant data is intentionally omitted; hence, it's important to include all important variables. Ultimately, preparing the data in this way allows for more accurate and efficient analysis, whether that be descriptive statistics, such as summarizing data through graphing or calculating averages, or inferential statistics, which uses probability to draw conclusions and make predictions.