10 Negative Correlation Examples

negative correlation examples and definition

A negative correlation is a relationship between two variables in which one variable decreases as the other increases.

As a negative correlation example from psychology, one might observe a negative correlation between happiness and the number of hours worked; that is, as working time increases, contentment diminishes.

From poverty and life expectancy to crime rates and education levels, as well as employment rates and inflation – negative correlations can be found in many areas.

Researchers can quantify the strength of a negative correlation between two variables and measure its effect on one another by utilizing techniques such as regression analysis.

This process makes it possible to assess how changes in one variable may influence the other.

Negative correlations can be a powerful tool for researchers, allowing them to uncover and reveal cause-and-effect relationships between various aspects of our world.

By studying these connections, we can gain much-needed insights into the environment around us.

Definition of Negative Correlation

Economics, math, statistics, psychology, and philosophy all study a phenomenon known as negative correlation: the relationship between two variables where an increase in one causes a decrease in the other.

The relationship between variables is inverse: as one increases, the other decreases. For instance, when temperatures rise, snowfall diminishes accordingly.

According to psychologists Hinote and Wasserman (2020),

“…negative (or inverse) correlation describes a situation when one variable increases the other systematically decreases, or vice versa” (p. 56).

Moreover, a negative correlation is an indication of the strength of the association between two variables and carries heavy implications for understanding how diverse phenomena interact with one another.

Simply, negative correlations can be used to explain why certain phenomena occur, such as why an increase in one variable causes a decrease in the other.

Negative Correlation Examples

  • Negative Correlation in Psychology: When interpersonal conflict increases, satisfaction in relationships decreases. This is because conflicts can erode trust, communication, and intimacy between individuals.
  • Negative Correlation in Sociology: As poverty rises, life expectancy decreases. This is because poorer people have less access to nutrition, healthcare, and other goods and services that help achieve a healthy lifestyle.
  • Negative Correlation in Education: Higher student-teacher ratios are correlated with lower student achievement scores. When there are too many students per teacher, it becomes difficult for individual attention to be given, which can ultimately impact student performance negatively.
  • Negative Correlation in the Environment: A study of air pollution and asthma rates reveals a clear correlation: the higher the levels of air contamination, the poorer out health becomes. We could also state this as a positive correlation: when there is more smog in our atmosphere, we experience more asthma.
  • Psychology: When a student’s procrastination increases, their academic performance decreases. This is because putting off tasks can lead to incomplete work, or increase stress levels, or cause missed deadlines.
  • Medicine: Excessive sugar intake has been linked to decreased oral hygiene, as it not only stimulates bacterial growth but also weakens tooth enamel.
  • Consumer Economics: As interest rates go up, house prices go down. This is because high interest rates make it harder to get a mortgage. With less demand in the market, the less people will be inclined to buy a house.
  • Consumer Economics: When mortgage interest rates surge, consumers feel the pinch of extra housing costs and decrease their spending in other areas. Consequently, an upswing in these rates leads to a drop in discretionary spending.
  • Biology: When consumption of ‘adult drinks’ rises, mental acuity and physical coordination decrease sharply. It is for this reason that it is illegal to drive when under the influence.
  • Consumer Economics: Higher unemployment rates coincide with reduced consumer confidence levels – when people don’t feel secure about their jobs, they tend to hold back from spending money or taking risks financially, suppressing economic growth over time if left unchecked.
  • Consumer Economics: As the weather increases, consumption of ice cream goes down. This is because our bodies crave cold foods more during hot weather than cold weather.
  • Sports Sciences: As exercise levels increase, risk of cancer decreases. As a result, most government health agencies recommend a minimum of 30 minutes of cardiac exercise per day.

Types of Correlations

While a negative correlation describes a relationship between two variables that decreases when one increases, a positive correlation is an opposite. A zero correlation implies no relationship between the two variables at all.

  • Negative correlation is a phenomenon in which two variables are related so that an increase in one of the variables leads to a decrease in the other and vice versa (DePoy & Gilson, 2016).
  • Positive correlation, on the other hand, is when two variables are related such that an increase in one variable results in an increase in the other (DePoy & Gilson, 2016). For example, an increase in sugar consumption will lead to decreased dental health (negative correlation). In contrast, an increase in exercise leads to increased physical fitness (positive correlation). In both cases, there is a direct relationship between the two variables but with different outcomes – one negative and one positive.
  • Zero correlation describes two variables that are completely unrelated to each other. It means that changes in one variable will not affect the other in any way (DePoy & Gilson, 2016).
  • Illusory correlation happen when two variables (people, events, or behaviors), are perceived to have a relationship, when in fact, there is no logical reason for them to be correlated. For example, if you see that a coin has flipped heads six out of six times, you may think that heads is likely to turn up again the next time. This is an illusion. There is still a 50/50 chance that tails will turn up.

How to Determine Negative Correlation

The Pearson Correlation Coefficient formula is one of the most commonly used methods for determining the strength of negative correlations between two variables.

This formula considers the mean and standard deviation of both variables, as well as the covariance between them.

The formula for calculating Pearson Correlation Coefficient is as follows:

Correlation(X,Y) = Covariance(X,Y) / (StdDev(X)*StdDev(Y)),

where covariance measures how two variables change together, StdDev is the standard deviation of each variable, and X and Y are your two variables (Sharma, 2019).

If you plug in your data points and calculate a result that is anywhere between -1 to 0, then you have established a negative correlation between the two variables.

Negative Correlation Strength

Negative correlation strength is measured on a scale from -1 to 0, with -1 being the strongest possible level of correlation and 0 indicating no correlation at all.

The closer the correlation coefficient gets to -1, the stronger the negative relationship between two different variables (McMillan, 2008).

For example, let’s say we’re looking at temperature and precipitation data for a specific area over time. A data set that showed temperatures were decreasing when rain was increasing would indicate a strong negative relationship.

If we ran our analysis and got back a correlation coefficient of -0.9, it would be safe to assume that there was indeed a strong negative connection between these two variables.

Alternatively, if we got back a coefficient of only -0.5 when running our analysis, this would indicate that while there is still some connection between these two variables (temperature decreasing when rain increases), it isn’t as strong as before (−0.9).

It could mean that other factors influence this relationship besides just temperatures and rainfall.

Importance of Negative Correlation

Negative correlation is an important concept in data analysis, as it helps better understand the relationships between two variables. 

For example, if studying how temperature affects a species’ growth rate, a negative correlation might indicate that as temperatures increase, the growth rate of the species decreases.

This knowledge can be used to make informed decisions about what actions should be taken to preserve or improve long-term growth rates for the species.

In predictive analytics, understanding negative correlations can help us anticipate and respond to changes in trends.

For instance, analyzing correlations can reveal connections between stock prices and other macroeconomic indicators when working with financial data sets.

By recognizing those connections, better predictions can be made about future market movements, and investment opportunities can be identified.

Negative correlation may also provide insight into cause-and-effect relationships between otherwise unrelated variables that are not easy to identify.

A classic example of this is the strong negative correlation between smoking tobacco and life expectancy: as more people smoke cigarettes over time, life expectancy tends to decline.

Understanding this correlation allows public health professionals to target interventions and create campaigns that reduce smoking prevalence.

Negative Correlation in Portfolio Diversification 

Negative correlations in portfolio diversification arise when two investments have an inverse relationship. For example, as one asset goes up, the other goes down, and vice versa (Pula et al., 2012).

This type of correlation is used to reduce risk in a portfolio, as it spreads out losses over different assets and can help protect investors from large market swings.

Negative correlations are usually found between stocks and bonds or stocks and commodities such as gold or oil.

For example, bonds tend to increase in value when stocks go down due to their safe-haven status. Similarly, when oil prices drop, gold prices tend to rise as investors flock toward the safety of precious metals.

Another way negative correlations can be used in portfolio diversification is by investing in different geographic regions or market sectors (Lossen, 2007).

Investing in emerging markets like India or China may provide some protection against investments in established markets like the US or UK because rises and falls may not necessarily occur simultaneously across countries or regions.

Negative correlation can also be exploited through short selling, where a trader will sell borrowed shares with the expectation that they will fall in price so that they can be bought back at a lower cost later on.

This strategy helps protect against losses if a trader is wrong about their bet and provides an additional layer of protection for an investor’s portfolio.


Negative correlation is an important concept in data analysis, predictive analytics, and portfolio diversification.

It can provide insight into cause-and-effect relationships between variables that are not obvious and can be used to identify investment opportunities or protect against losses.

When analyzing correlations, it is important to remember that correlation does not imply causation, and further research is needed to understand the underlying relationships between variables.

However, properly understanding negative correlations can be valuable for making better data-driven decisions.


DePoy, E., & Gilson, S. (2016). Social work research and evaluation. SAGE Publications.

Hinote, B. P., & Wasserman, J. A. (2020). Social and behavioral science for health professionals. Rowman & Littlefield Publishers.

Lossen, U. (2007). Portfolio strategies of private equity firms. Springer Science & Business Media.

Mcmillan, J. H. (2008). Assessment essentials for standards-based education. Corwin Press.

Pula, J. S., Berisha, G., & Ahmeti, S. (2012). The impact of portfolio diversification in the performance and the risk of investments of kosovo pension savings trust. Journal of Business and Economics3(3), 198–211. https://doi.org/10.15341/jbe(2155-7950)/03.03.2012/005

Sharma, J. K. (2019). Business statistics. Vikas Publishing.

Viktoriya Sus

Viktoriya Sus (MA)

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Viktoriya Sus is an academic writer specializing mainly in economics and business from Ukraine. She holds a Master’s degree in International Business from Lviv National University and has more than 6 years of experience writing for different clients. Viktoriya is passionate about researching the latest trends in economics and business. However, she also loves to explore different topics such as psychology, philosophy, and more.

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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

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