{"id":1602,"date":"2019-02-01T19:41:58","date_gmt":"2019-02-01T19:41:58","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=1602"},"modified":"2023-02-05T21:22:12","modified_gmt":"2023-02-05T21:22:12","slug":"stats-training-materials-basic-statistical-concepts","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/stats-training-materials-basic-statistical-concepts\/","title":{"rendered":"Stats Training Materials – Basic Statistical Concepts"},"content":{"rendered":"

A sound grasp of basic statistical concepts is essential to have any hope of acquiring the mindset of a statistical thinker and to be able to use statistical methods.\u00a0 My introductory course “The 6 Concepts of Statistical Thinking<\/em><\/a>” lays the foundations in the following.<\/p>\n

    \n
  1. Probability<\/span><\/strong> – the difference between conditional & absolute probability.<\/li>\n
  2. Risk<\/span><\/strong> – why it is an extension of probability and the importance of alpha (false positive) and beta (false negative) risk.<\/li>\n
  3. Expectation<\/span><\/strong> – how to summarise a dataset into one number which measures its location.<\/li>\n
  4. Variance<\/span><\/strong> – how to measure the spread of a dataset.<\/li>\n
  5. Distribution<\/strong><\/span> – how to describe the shape of a dataset.<\/li>\n
  6. Correlation<\/span><\/strong> – how to measure the relationship between two variables and understand the two golden rules of correlation.<\/li>\n<\/ol>\n

    <\/p>\n

    Below is a list of resources to help improve your understanding of statistical concepts.<\/p>\n

    \n

    A. Summary Statistics<\/strong><\/span><\/h4>\n

    When we summarise a dataset, we are seeking to get a first impression of what it is telling us.\u00a0 We do this by calculating, tabulating and charting a variety of statistics that allows us to characterise its Location<\/strong> (Expectation), Spread<\/strong> (Variance) and Shape<\/strong> (Distribution).<\/p>\n

    The following posts are examples of summarising some well known datasets with the purpose of being able to place the latest data point in context.<\/p>\n

      \n
    1. UK Weather tracker<\/a> – summarises monthly weather variables in the UK.<\/li>\n
    2. UK Economy tracker<\/a> – summarises quarterly economic statistics in the UK.<\/li>\n
    3. Donald Bradman, the 9 Sigma GOAT<\/a> – I show how exceptional the Don was relative the Spread & Shape of his peers.<\/li>\n
    4. What are you expecting for your team this season?<\/a> – Looks at the first 25 years of the English Premier League in football and what it takes to achieve certain outcomes.<\/li>\n<\/ol>\n

      When it comes to charting data, the most common charts people use in Excel are line, bar and scatter plots.\u00a0 In the latest version, Excel has finally added a feature to produce Box Plots which are really worth adding to your armoury.\u00a0 Link A4 above is an example as well as link B1 below.<\/p>\n

       <\/p>\n

      \n

      B. Data Driven Decision Making<\/strong><\/span><\/h4>\n

      There are many definitions of Statistical Thinking but a common one is “Data Driven Decision Making”.\u00a0 A statistical thinker with a strong grasp of basic statistical concepts will find it easier to make decisions using data.\u00a0 In many cases, decisions can be made with summary statistics and basic charts and the list below are some examples.<\/p>\n

        \n
      1. Is the land safe for human activities?<\/a>\u00a0 I was the lead author of a professional guidance document for the contaminated land industry which explains how statistics (specifically dot & box plots and confidence intervals) can be used to make decisions on whether land is safe or not.<\/li>\n
      2. Another form of data driven decision making is deciding whether to take note of a latest piece of research in the news.\u00a0 Should you pay attention to its results or not?\u00a0 What are the markers of a decent research project and what can be ignored?\u00a0 To help you with this, I came up with the concept of an Evidence Hierarchy<\/strong> or Circle and you can find out more by clicking this link<\/a>.<\/li>\n<\/ol>\n

         <\/p>\n

        \n

        C. Statistical Thinking & Gender Pay Gaps<\/strong><\/span><\/h4>\n

        All organisations with 250 or more employees are now required to publish data on their gender pay gaps.\u00a0 Whilst I am supportive of the principle, I can see a myriad number of ways this data will be misused and misinterpreted through a lack of understanding of basic statistical concepts.\u00a0 Therefore I am blogging about gender pay gaps with the aim of improving people’s understanding and you can find all my blogs in the Diversity section of my blog<\/a>.<\/p>\n

        The following posts within that section are especially relevant to understanding basic statistical concepts.<\/p>\n

          \n
        1. What gender pay gap data tells us, what it doesn’t tell us and how it can be misused<\/a><\/li>\n
        2. How can you tell if an employer has published an incorrect median gender pay gap?<\/a><\/li>\n
        3. Why Gender Pay Fingerprints are superior to Gender Pay Gaps.<\/a><\/li>\n
        4. The difference between Unequal Pay & Gender Pay Gaps<\/a><\/li>\n
        5. What is the Gender Pay Gap at Novartis UK?<\/a><\/li>\n
        6. 10 quick and easy ways to close your gender pay gap without trying very hard<\/a><\/li>\n<\/ol>\n

          For a full list of my blogs on gender pay gaps organised by theme like this page, please follow this link<\/a>.<\/p>\n

           <\/p>\n

          \n

          D. The 9 Concepts of Statistical Thinking<\/strong><\/span><\/h4>\n

          My course “The 6 Concepts of Statistical Thinking<\/a>” was first designed about 15 years ago.\u00a0 In that time, a lot of progress has been made in technology and in this article “The future of Statistical Thinking<\/a><\/em>” published in Significance magazine in December 2014, I pondered whether or not I should change my course title to “The 9 Concepts of Statistical Thinking<\/em>“.<\/p>\n

           <\/p>\n

          \n

          E. Recommended Books<\/strong><\/span><\/h4>\n

          Here is a list of books I can recommend if you would like to improve your understanding of basic statistical concepts.\u00a0 All appear in my training courses<\/a>.<\/p>\n

            \n
          1. “Conned Again, Watson!<\/a><\/strong>” by Colin Bruce – Published in 2002, this is my all time favourite book on statistics.\u00a0 What Colin Bruce does is write Sherlock Holmes stories where Holmes is a statistician and detective.\u00a0 Some stories are little contrived but Bruce has done a great job of capturing the spirit of Conan Doyle’s books as well as illustrating a wide range of statistical and economic concepts.<\/li>\n
          2. “Reckoning with Risk”<\/a> <\/strong>by Gerd Gigerenzer – Published in 2002, I think it is a great shame that Gerd’s ideas on how to explain and present risk have not been taken up more widely.\u00a0 The human race can confuse itself terribly when it comes to risk especially when they are presented as probabilities and percentages.\u00a0 Gerd’s central insight is that numerical frequencies are much more likely to be understood and he writes about numerous examples of how this can be applied.<\/li>\n
          3. “<\/strong>Thinking<\/strong> Fast & Slow<\/strong><\/a>” by Daniel Kahneman” – Published in 2011, this book explores in great depth why the human race is so poor at statistics.\u00a0 He backs his work with a huge range of experiments that have been undertaken to test this and I think you will find this very illuminating.\u00a0 It is a big book but it is worth persevering with.<\/li>\n<\/ol>\n

             <\/p>\n

            \n

            F. Free online resources<\/strong><\/span><\/h4>\n

            I will add some links to useful resources as I come across these.<\/p>\n

              \n
            1. A really useful guide to probability distributions<\/a> and how they are related to each other.\u00a0 I will be making a lot of use of this!<\/li>\n<\/ol>\n
              \n

               <\/p>\n

              If you would like to book a training course in Basic Statistical Concepts<\/a>, then please contact me<\/a>.<\/p>\n

              For more information about my other training courses in statistics, please visit my Statistical Training homepage<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

              A sound grasp of basic statistical concepts is essential to have any hope of acquiring the mindset of a statistical thinker and to be able to use statistical methods.\u00a0 My introductory course “The 6 Concepts of Statistical Thinking” lays the foundations in the following. Probability – the difference between conditional & absolute probability. Risk – […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[7],"tags":[37,35,40,95,96],"class_list":{"0":"post-1602","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-stats-training","7":"tag-data-journalism","8":"tag-evidence-hierarchy","9":"tag-presenting-data","10":"tag-statistical-concepts","11":"tag-statistical-thinking","12":"entry","13":"override"},"_links":{"self":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1602","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/comments?post=1602"}],"version-history":[{"count":19,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1602\/revisions"}],"predecessor-version":[{"id":4883,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1602\/revisions\/4883"}],"wp:attachment":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media?parent=1602"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/categories?post=1602"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/tags?post=1602"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}