1. result deviation: missing data may lead to deviation of research results. If the missing data is not random, the sample may be biased, thus affecting the accuracy and reliability of the research results. For example, if the lack of data is mainly concentrated on a specific group or variable, then the research results may not necessarily represent the whole population.
2. Reduced statistical validity: Lack of data will reduce the statistical validity of the study. This is because the lack of data will reduce the sample size, thus reducing the stability and repeatability of the research results. In addition, the lack of data may also reduce the efficiency of testing research hypotheses, thus affecting the explanatory power of research results.
3. Increased model error: In many studies, researchers need to use complex statistical models to analyze data. However, missing data may lead to the increase of model error. This is because the missing data will affect the estimation of model parameters, thus affecting the accuracy and reliability of the model.
4. Reduced credibility of the conclusion: Due to the above reasons, the lack of data may reduce the credibility of the conclusion of the paper. If readers find a lot of missing data in the paper, they may doubt the reliability of the research results.
Therefore, researchers should try to avoid the lack of data when conducting research. If there are missing data, researchers should take appropriate measures to deal with these missing data to reduce its impact on the research results.