Kolmogorov strong law of large numbers

Target entity: Kolmogorov strong law of large numbers
Context triple: [Erdős–Rényi law of large numbers, relatedTo, Kolmogorov strong law of large numbers]

• A. Kolmogorov's law of the iterated logarithm
  Kolmogorov's law of the iterated logarithm is a fundamental result in probability theory that precisely characterizes the almost-sure fluctuations of partial sums of independent random variables between the law of large numbers and the central limit theorem.
• B. Khinchin's law of the iterated logarithm
  Khinchin's law of the iterated logarithm is a fundamental result in probability theory that precisely characterizes the almost-sure fluctuations of partial sums of independent random variables on the scale of the square root of twice the product of their variance and the iterated logarithm of the sample size.
• C. Gnedenko–Kolmogorov limit theorem
  The Gnedenko–Kolmogorov limit theorem is a fundamental result in probability theory that characterizes the limiting distributions of properly normalized sums of independent random variables, generalizing the classical central limit theorem to stable laws.
• D. Kolmogorov zero–one law
  The Kolmogorov zero–one law is a fundamental result in probability theory stating that certain events determined by the tail behavior of independent random variables must have probability either zero or one.
• E. Erdős–Rényi law of large numbers
  The Erdős–Rényi law of large numbers is a refinement of the classical law of large numbers that provides precise asymptotic behavior and convergence rates for sums of independent random variables, developed by mathematicians Pál Erdős and Alfréd Rényi.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Kolmogorov strong law of large numbers
Target entity description: The Kolmogorov strong law of large numbers is a fundamental theorem in probability theory stating that the sample average of independent, identically distributed random variables converges almost surely to their expected value under suitable conditions.

• A. Kolmogorov's law of the iterated logarithm
  Kolmogorov's law of the iterated logarithm is a fundamental result in probability theory that precisely characterizes the almost-sure fluctuations of partial sums of independent random variables between the law of large numbers and the central limit theorem.
• B. Khinchin's law of the iterated logarithm
  Khinchin's law of the iterated logarithm is a fundamental result in probability theory that precisely characterizes the almost-sure fluctuations of partial sums of independent random variables on the scale of the square root of twice the product of their variance and the iterated logarithm of the sample size.
• C. Gnedenko–Kolmogorov limit theorem
  The Gnedenko–Kolmogorov limit theorem is a fundamental result in probability theory that characterizes the limiting distributions of properly normalized sums of independent random variables, generalizing the classical central limit theorem to stable laws.
• D. Kolmogorov zero–one law
  The Kolmogorov zero–one law is a fundamental result in probability theory stating that certain events determined by the tail behavior of independent random variables must have probability either zero or one.
• E. Erdős–Rényi law of large numbers
  The Erdős–Rényi law of large numbers is a refinement of the classical law of large numbers that provides precise asymptotic behavior and convergence rates for sums of independent random variables, developed by mathematicians Pál Erdős and Alfréd Rényi.
• F. None of above. chosen