Have you ever watched a sports match and heard commentators talking about ‘the highest scoring player’, ‘the winning streak’, or ‘the average score’? These terms are all linked to ‘statistics’, the collection, analysis, interpretation, presentation, and organization of data. Knowing how to express statistical data, trends, and analysis using the correct English collocations can be a game-changer, not only in understanding sports commentary, but also in various other fields such as business, health, and research. It can even make your day-to-day conversations sound more professional and knowledgeable. Interested?
Let’s dive into our list of 37 commonly used English collocations related to statistics:
1. Latest figures: The most recent statistical data.
Example: The latest figures show an increase in sales.
2. Upward trend: A pattern showing increasing numbers.
Example: The upward trend in housing prices concerns potential buyers.
3. Downward spiral: A rapid fall in number or amount.
Example: His health seemed to be in a downward spiral.
4. Annual growth: The increase in size or amount over a year.
Example: The company reported strong annual growth.
5. Steady decrease: A consistent drop in numbers.
Example: There has been a steady decrease in cigarette sales.
6. Statistically significant: A result that is not likely to happen by chance.
Example: The difference in the two results is statistically significant.
7. Margin of error: The amount by which a set of statistical results might be wrong.
Example: There is a margin of error of 2% in the survey.
8. Gather data: To collect statistical information.
Example: We need to gather data on customer satisfaction.
9. Analyze findings: To examine the results of data.
Example: The team will analyze findings from the study.
10. Conduct a survey: To perform a process of asking a group of people questions to collect data.
Example: We will conduct a survey to understand customer preferences.
11. Measure performance: To gauge how well something or someone is doing.
Example: We need to measure performance to identify areas of improvement.
12. Quantitative analysis: An examination based on numbers.
Example: The quantitative analysis showed that our efforts were effective.
13. Qualitative research: Research based on observations, not numbers.
Example: We use qualitative research to understand user behavior.
14. Benchmark statistics: A standard or point of reference against which things may be compared.
Example: Our benchmark statistics will help us measure progress.
15. Mean average: The total sum divided by the number of values.
Example: The mean average of scores was 85.
16. Mode value: The value that appears most frequently in a data set.
Example: The mode value of the dice throw was 4.
17. Median number: The middle number in a sorted list of numbers.
Example: The median number of our sales last week was 120.
18. Normal distribution: When the data is symmetrically distributed around the mean.
Example: Heights in a population often follow a normal distribution.
19. Outlier data: A data point that differs significantly from other similar points.
Example: The research team removed outlier data to get a more accurate result.
20. Central tendency: A measure that represents the center point of a dataset.
Example: The mean, median, and mode are measures of central tendency.
21. Inflation rate: The rate at which the general level of prices for goods and services is rising.
Example: The inflation rate has been steadily rising this year.
22. Unemployment figures: The number of people who do not have a job.
Example: The government is concerned about the high unemployment figures.
23. Economic indicators: Statistical data that show general trends in the economy.
Example: Inflation and unemployment rates are key economic indicators.
24. Population density: The number of people per unit of area.
Example: The population density in the city is very high.
25. Poverty line: The minimum level of income deemed adequate in a particular country.
Example: Many people in the country live below the poverty line.
26. Breakdown of figures: Detailed analysis or summary of statistical data.
Example: The report gives a breakdown of figures for each department’s spending.
27. Cite statistics: To mention or use statistical data as evidence.
Example: He cited statistics to prove his point.
28. Frequency distribution: The arrangement of a dataset in ascending order of magnitude with their corresponding frequencies.
Example: The teacher explained frequency distribution with a simple example.
29. Range of data: The difference between the highest and lowest values in a dataset.
Example: The range of data for the experiment was quite broad.
30. Calculate probability: To determine the likelihood of an event happening.
Example: We need to calculate probability to assess risk.
31. Hypothesis testing: A procedure to test if a statement about a population parameter is correct.
Example: We use hypothesis testing in our research methodology.
32. Causal relationship: Where one variable influences the other.
Example: The study explores the causal relationship between diet and health.
33. Correlation coefficient: A statistical measure of the degree to which changes to the value of one variable predict change to the value of another.
Example: The correlation coefficient between the two variables was 0.89.
34. Variance analysis: A quantitative investigation of the difference between actual and planned behavior.
Example: Variance analysis helps us understand where we deviated from our budget.
35. Regression analysis: A set of statistical processes for estimating the relationships among variables.
Example: We used regression analysis to predict future sales.
36. Scatter plot: A graphical representation of a correlation between two variables.
Example: The scatter plot shows a positive correlation between age and income.
37. Confidence interval: An estimated range of values likely to include an unknown population parameter.
Example: The confidence interval gives us a range in which the true value likely lies.
By mastering these statistics related collocations, you can comprehend and converse about data and analysis more naturally. Whether you’re studying, working, or just curious, these phrases will enable you to express numerical data in a comprehensive and professional manner. Remember, practice makes perfect, so start including these in your conversations and see the difference.
