import statspreferred_model = max(pref_ratios, key=pref_ratios.get)successes = preferences.count(preferred_model)n = len(preferences) - preferences.count(None)p_value = stats.binom_test(successes, n, p=0.5, alternative="two-sided")print(    f"""The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}times out of {n} trials.""")    The p-value is 0.00040. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),    then there is a 0.04025% chance of observing the OpenAI Functions Agent be preferred at least 18    times out of 20 trials._1. Note: Automated evals are still an open research topic and are best used alongside other evaluation approaches. LLM preferences exhibit biases, including banal ones like the order of outputs. In choosing preferences, "ground truth" may not be taken into account, which may lead to scores that aren't grounded in utility._PreviousAgent VectorDB Question Answering BenchmarkingNextData Augmented Question AnsweringStep 1. Create the EvaluatorStep 2. Select DatasetStep 3. Define Models to CompareStep 4. Generate ResponsesStep 5. Evaluate PairsEstimate Confidence IntervalsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.