"Outliers" Example Sentences
1. The data set had a few outliers that were drastically skewing the results.
2. Removing the outliers from the sample size increased the accuracy of the data analysis.
3. The researcher had to account for the outliers in their statistical analysis.
4. The outlier data points were significantly different from the rest of the data.
5. It's important to identify outliers in the data before drawing any conclusions.
6. The graph showed a clear outlier point that didn't fit the trend of the rest of the data.
7. The outlier data was excluded from the final results of the study.
8. The outlier value resulted in a much higher standard deviation than expected.
9. The researcher realized that the outlier data was due to a measuring error.
10. The team spent hours analyzing the outlier data to find any potential explanations.
11. The outlier observation had a significant impact on the overall outcome of the study.
12. The outlier points in the graph indicated a potential problem with the data collection process.
13. The researchers concluded that the outlier data was a result of a sampling bias.
14. The data analyst decided to keep the outlier data in the analysis to see if it would affect the final result.
15. The outliers in the dataset made it difficult to see any clear trends or patterns.
16. The regression analysis indicated that the outlier points had a strong influence on the overall model.
17. The presence of outliers can make it harder to draw any meaningful conclusions from the data.
18. The researcher ran a sensitivity analysis to see how the outlier data impacted the results.
19. The outlier data didn't match any of the patterns or trends that were expected.
20. The outlier values were so far from the mean that they were considered extreme outliers.
21. The team had to decide how to handle the outlier data in order to accurately represent the results.
22. The outlier values had a significant impact on the normal distribution of the data.
23. The outlier data could have been caused by a number of different factors.
24. The presence of outliers could indicate a need to collect more data or adjust the research methodology.
25. The outlier values had a disproportionate impact on the final results of the study.
26. The outlier data points were removed after it was discovered that they were the result of a data entry error.
27. The team decided to run a robust regression analysis to account for the outlier data.
28. The researcher realized that the outlier data was actually a valid observation that needed to be included in the analysis.
29. The outlier data was referred to as "noise" and was not considered representative of the true data distribution.
30. The presence of outliers can be an indication of a non-normal distribution in the data.
Common Phases
- "We need to identify and remove the
outliers from our data;"
- "The
outliers are skewing our results; we need to address this before drawing any conclusions;"
- "The presence of
outliers is affecting the accuracy of our model; we need to take steps to mitigate their impact;"
- "We can't ignore the
outliers in our analysis; they could be indicative of important trends or anomalies;"
- "
Outliers can make it difficult to generalize findings; we need to explore their effect on our results."