To deliver value with our data we need to be confident that we are saying is correct and others need to be confident that the data, insight and forecasts we share with them are accurate. Unfortunately the measures we analyse will be affected by random chance and external and internal factors that can make 100% accuracy unachievable. In this box set we will.

• Share tools and techniques to allow you to measure the impact of chance and understand the limitations of the data.
• Discuss the ways that our data can become bias and ways we can manage this
• Teach you to find the right balance between cost/time and accuracy. And make sure the MI, Insight and Forecasts we share are “good enough”

Modules

An Introduction to Accuracy and confidence
Understand the key statistical considerations that will ensure your conclusions are well founded and can drive confidence. The separate Statistical Confidence box set is an opportunity to drive this further

Descriptive Statistics
In this module we be exploring the Descriptive Statistics tool in excel. We will cover:

• How to use descriptive statistics
• How to use the output to better understand your data
• Alternative ways of calculating

How to Calculate Standard Deviation
Standard deviation is an essential tool for statistical analysis. In this module, we will talk through how to calculate this manually

Descriptives Statistics Example - Using Averages
In this worked example we will be exploring the pitfalls to avoid when using averages. And when to use Mean, Median & Mode

Descriptive Statistics Example - Understanding our data
In this module, we are going to look at descriptive statistics in action and explore how we can use this to better understand our data.

Descriptive Statistics Example - Measuring Benefits
In this module, we are going to look at descriptive statistics in action and explore how we can use this to ensure we are measuring benefits correctly.

Descriptive Statistics Example – Calculating the Accuracy of your Forecast
In this module, we are going to look at a use of descriptive statistics that is essential to every organisation, and that is measuring the accuracy of a forecast. Or to put this another way, setting clear expectations around volatility.

Calculating Sample Size
This module covers:

• The necessity of calculating appropriate sample sizes for data analysis and the reliance on samples due to data limitations.
• Key considerations for sample size calculations, including bias avoidance, sample representativeness, and statistical concepts like margin of error and confidence intervals.
• Practical examples and the application of a sample size calculator to determine sample sizes for surveys, compliance checks, and comparisons.

Conditional Probability
Our data often throws up anomalies that can be counterintuitive. Million to one chances happen far more often than we would expect. In this module we will explore Statistical Probability and Bayes Theorem. A technique that will help you explain these anomalies and prevent you drawing incorrect conclusions.

Understanding & Managing Sample Bias
This module covers:

• The critical role of recognising and addressing sampling bias in data-related fields to ensure valid insights and decision-making.
• The necessity of being vigilant against bias in data analysis, highlighting strategies for identifying and correcting such biases.
• Techniques for mitigating bias, focusing on understanding different types of biases and the importance of scrutinising data sources for reliability and accuracy.

The cost of this mini series including accreditation is great value at £325 + VAT and free for students on our assisted learning pathway.