Quantitative analysis: From statistics to forecasting

Can everything be measured? Quantitative analysis provides clear answers based on statistics and mathematical models. It can help you identify patterns, predict trends, and make informed decisions. In this article, we will look at its main methods and application examples.

Quantitative analysis is a method of analyzing information based on mathematical methods and statistical data. This approach allows you to measure and evaluate information in numerical form, which facilitates the analysis and comparison of various parameters. Let’s take a closer look at the main elements of quantitative analysis and its application in various fields.

Fundamental approaches to quantitative analysis

Statistical analysis involves the application of various statistical methods to process and analyze data. This may include calculating means, variance, correlation, regression, and other statistical measures that reveal patterns in the data.
Time series analyzes changes in data over time. This approach is widely used to predict trends, seasonal fluctuations, and other time patterns.
Multivariate analysis examines the relationships between multiple variables simultaneously. This allows for the exploration of complex interactions between different aspects of the Statistical analysis involves the application of various statistical methods to process and analyze data.

An example of the integrated use of quantitative analysis approaches in research:

In 2023, a regression study was conducted on 35,014 fact-checked statements (2008–2023) by Snopes and PolitiFact. The results showed that false statements were 20% more likely to mention political elites, with those mentioning Democrats occurring 88% more often than false statements mentioning Republicans (26% less often) compared to true statements.

– The study used statistical methods to identify the relationship between the falsity of claims and the mention of politicians. Using the χ² test and logistic regression, the authors showed that false claims more often affect Democrats (+88.1%) and less often Republicans (-26.5%) compared to true ones. These calculations made it possible to quantitatively confirm the presence of political asymmetry in the spread of disinformation. Thus, it was found that false statements are 20% more likely to contain the names of political elites.

– The authors also used time series analysis to analyze the change in the share of false claims mentioning politicians between 2008 and 2023. They found consistent spikes in disinformation in election years, especially before the 2012, 2016, and 2020 elections.

Application of quantitative analysis

Economic Analysis: In economics, quantitative analysis is used to evaluate macroeconomic indicators, analyze market trends, forecast economic growth, and make financial decisions.

Machine learning and quantitative macroeconomic models were used to forecast China’s GDP growth, and their forecast accuracy was superior to classical econometric approaches, especially during stable periods.

Medical research. In medical research, quantitative analysis is used to analyze the effectiveness of drugs, identify risk factors for diseases, and evaluate the effectiveness of treatments and diagnostics.

A “model meta-analysis” approach was used to evaluate the efficacy of 12 drugs in primary progressive multiple sclerosis. The results showed that most drugs were significantly superior to placebo in terms of the proportion of patients without disability progression after 96 weeks.

Sociological research. In sociology, quantitative analysis is used to study social phenomena, identify trends and patterns of behavior, and assess the social structure and dynamics of society.

In February 2025, a quantitative study in Latvia showed that 47% of respondents aged 18-29 were not interested in politics, while among those aged 60-74, the figure was only 22%. A breakdown by gender and party membership was also provided.

Scientific research. In scientific research, quantitative analysis is used to statistically process experimental data, test hypotheses, analyze results, and identify patterns.

In the article “A Review of Quantitative Research Methods in Engineering Education from 2012 to 2022,” the authors described trends in how statistical tests, regressions, ANOVA, scaling, and other methods are used to analyze educational experiments and educational data.

Challenges of quantitative analysis
Although quantitative analysis is a powerful tool for data analysis, it also faces a number of challenges.

The need to choose the right analysis methods.

If you choose the wrong method, the results will be meaningless or false.

Imagine that you are faced with the task of predicting the cost of apartments in 5 years. The wrong method in this case is linear regression (assumes that prices grow evenly). In fact, the real estate market depends on crises, demand, inflation it is not a straight line.

What to do?

– If the data changes over time (stock price, weather) — time series methods are needed.

– If you want to predict one thing based on another (for example, salary based on experience) — regression analysis.

– If you are comparing groups (the effect of two drugs) — statistical tests (t-test, ANOVA).

Interpretation of results.

Even correct figures can be misunderstood.

Let’s say that statistics showed a positive correlation between the number of firefighters on call and the amount of damage from such fires. Absurd conclusion: “Firefighters make fires worse!” Reality: more teams are sent to major incidents. That is, in fact, the cause of the damage is the scale of the emergency, and not the number of firefighters who arrived.

What to do?

– Always check for hidden factors (confounders).

– Remember that correlation ≠ causation.

Cleaning data from outliers and errors.

“Dirty” data distorts the picture.

You calculate the average salary in the company: 10 employees receive 50,000 rubles each, and one — 5,000,000 rubles (director). Without cleaning, the average salary will be 500,000 rubles (but this is misleading). After cleaning, we will see the median — 50,000 rubles (the real picture).

What to do?

– Look for anomalies (e.g. person’s age “150 years” or negative income).

– Use robust methods (median instead of mean).

Considering the limitations of models and assumptions.

Any model simplifies reality.

Meteorologists predicted rain with an 80% probability. In reality, there is no rain. Why? The model takes into account temperature, humidity, but cannot predict a sudden wind that will disperse the clouds.

What to do?

– Know the assumptions of the model (e.g., “linear regression assumes a direct relationship”).

– Add caveats: “The forecast is correct unless X happens.”

Conclusion: how to avoid mistakes?

Methods — Choose like a doctor: not “any medicine”, but what fits the symptoms.
Interpretation — Ask: “What else could have influenced?”
Data — Clean like vegetables before cooking: remove inedible parts to avoid poisoning.
Models — Remember that this is a “simplified map”, not the area itself.

Thus, it is important to understand that quantitative analysis is not a magic wand, but a tool that requires:

– Good data (complete, representative, clean);

– Good methods (appropriate to the task, without overfitting);

– Critical interpretation (understanding the limitations and context);

– Ethical approach (avoiding discrimination, protecting privacy).

Quantitative analysis has penetrated almost all spheres of life — from science to entertainment. The more data humanity generates, the more important it is to be able to process it correctly. At the same time, it is important to remember that “numbers don’t lie, but liars can count.” Quantitative analysis provides accurate results, but its success depends on the quality of the data and its proper application. Having mastered its methods, you can find hidden patterns and make more informed decisions.