Free Online Statistical Analysis Tool
Using the Basic Statistics Calculator is very simple:
The Basic Statistics Calculator is used in various scenarios:
Calculate basic statistics from experimental data or survey results for inclusion in research papers and reports. Mean, standard deviation, and median are essential for summarizing research findings.
Analyze business data such as sales figures, customer satisfaction scores, and product ratings to identify trends and patterns. The median is particularly useful for salary analysis as it's less affected by outliers.
Calculate standard deviation of product dimensions, weight, and performance data to assess quality variance. This provides foundational data for control charts.
Analyze test scores and class grades to calculate average scores, median, and standard deviation. This helps understand overall academic performance and grade distribution.
Calculate statistics for health indicators such as blood pressure, temperature, BMI, and blood glucose to compare against normal ranges and analyze temporal changes.
Calculate average returns and standard deviation (volatility) for stock price fluctuations, investment returns, and risk assessment to inform investment decisions.
Calculate mean and standard deviation for weather data such as temperature, precipitation, and humidity to identify climate patterns and detect anomalies.
Basic statistics (descriptive statistics) are numerical measures that summarize the characteristics of a dataset. Below are explanations of key statistical measures.
These measures represent the 'center' or 'typical value' of the data.
The sum of all data values divided by the number of values. The most common representative value, but susceptible to outliers. Formula: Mean = Σx / n
The middle value when data is arranged in order. Less affected by outliers, making it more appropriate than the mean for skewed distributions (e.g., income distribution).
The most frequently occurring value in the dataset. Useful for analyzing categorical data (e.g., survey responses).
These measures indicate how spread out the data is.
The difference between the maximum and minimum values. Shows the overall spread of data but is highly influenced by outliers. Formula: Range = Maximum - Minimum
A measure of how far each data point is from the mean. Larger values indicate greater data dispersion. There are two types: sample variance and population variance.
The square root of variance. Expresses data dispersion in the same units as the original data, making it easier to interpret. One of the most important indicators in statistical analysis.
The difference between the third quartile (Q3) and first quartile (Q1). Represents the range of the middle 50% of data and is a measure of dispersion less affected by outliers. Formula: IQR = Q3 - Q1
In statistics, calculation methods differ depending on whether the data represents a 'sample' or the entire 'population'. Sample variance and standard deviation use 'n-1' in the denominator, while population variance and standard deviation use 'n'. For general data analysis, using sample statistics (n-1) is recommended.
No need to perform complex statistical calculations manually. Simply enter your data and get instant results, saving significant time.
Completely eliminates calculation errors that commonly occur with manual calculations or calculators. Accurate statistics are guaranteed.
Calculate multiple statistical indicators (mean, median, standard deviation, quartiles, etc.) with a single operation.
Calculating basic statistics is the starting point for data analysis. Quickly grasp the overall picture of your data and determine the direction for further analysis.
Helps students and beginners learning statistics verify their calculations and understand the meaning of statistical measures.
No software installation or registration required. Use it anytime, anywhere with just a browser, completely free.
The mean is susceptible to outliers (extremely large or small values), so it's important to check it alongside the median.
Select appropriate statistics based on your data distribution and purpose. Example: median for income data, mean and standard deviation for test scores.
Combining basic statistics with graphs such as histograms and box plots provides deeper understanding of data distribution.
Variance is in the square of the original data units (e.g., if data is in cm, variance is in cm²). Standard deviation (in original units) is more convenient for interpretation.
The mean considers all data points but is susceptible to outliers. The median is less affected by outliers, making it more appropriate for data with extreme values such as income distribution or real estate prices. Checking both helps identify data skewness.
Sample standard deviation is calculated from a subset (sample) of the entire population using 'n-1' in the denominator. Population standard deviation is calculated from all data (population) using 'n' in the denominator. For general data analysis, using sample standard deviation is recommended.
When all data values are different, or when multiple values occur with the same frequency, there is no clear mode, so 'N/A' is displayed.
This tool displays results to 4 decimal places. If you need higher precision, we recommend using specialized statistical software (R, Python, etc.).
For browser performance, we recommend up to 10,000 data points. For larger datasets, we recommend using specialized statistical software.
This tool uses the values at positions that divide the data into four equal parts as quartiles. Q1 is the 25th percentile, Q2 (median) is the 50th percentile, and Q3 is the 75th percentile.
Yes, both negative numbers, decimals, and integers are supported. Scientific notation (1.23e-4, etc.) is also accepted.
Click the "Copy Results" button to copy the calculation results to your clipboard. You can then paste them into applications like Excel or Word.
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