## A quick argument for why we don’t accept the null hypothesis

Introduction

When doing hypothesis testing, an often-repeated rule is ‘never accept the null hypothesis’. The reason for this is that we aren’t making probability statements about true underlying quantities, rather we are making statements about the observed data, given a hypothesis.

We reject the null hypothesis if the observed data is unlikely to be observed given the null hypothesis. In a sense we are trying to disprove the null hypothesis and the strongest thing we can say about it is that we fail to reject the null hypothesis.

That is because observing data that is not unlikely given that a hypothesis is true does not make that hypothesis true. That is a bit of a mouthful, but basically what we are saying is that if we make some claim about the world and then we see some data that does not disprove this claim, we cannot conclude that the claim is true.…

## p-values: an introduction (Part 1)

The starting point

This is the first of (at least) 3 posts on p-values. p-values are everywhere in statistics- especially in fields that require experimental design.

They are also pretty tricky to get your head around at first. This is because of the nature of classical (frequentist) statistics. So to motivate this I am going to talk about a non-statistical situation that will hopefully give some intuition about how to think when interpreting p-values and doing hypothesis testing.

My New Car

I want to buy a car. So I go down to the second hand car dealership to get one. I walk around a bit until I find one that I like.

I think to myself: ‘this is a good car’.

Now because I am at a second-hand car dealership I find it appropriate to gather some data. So I chat to the lady there (looks like a bit of a scammer, but I am here for a deal) about the car.…

## Learn Wolfram Mathematica in the Cloud Part 6

Today we delve into Associations aka Dictionaries in languages like Python

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## Learn Wolfram Mathematica in the Cloud part 5

Let’s do some list FU, a kind of Kung Fu with Wolfram language lists

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## Learn Wolfram Mathematica in the cloud part 4

Diving deeper into lists

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## Learn Wolfram Mathematica in the cloud part 3

Dipping into Lists

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## Learn Wolfram Mathamatica Part 2

Today we see how to use Wolfram language as a Calculator using the Notebook environment

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## Introduction to Wolfram Mathematica programming

Mathematica is becoming an indispensable tool for doing all kinds of computation and it is important to know how to use it as it will leverage your problem-solving skills, allowing you focus on higher level issues of modelling solutions rather than focusing too much on calculational details.

I will be posting short lessons regularly and the good news is you don’t need to install anything locally as all examples can be run online. If you need to do any form of extensive programming you can always go to the Wolfram Cloud and click on Programming Lab and get access for free.

Instructions on running code

If you don’t have a Wolfram ID, create one as this will give you access to the Wolfram Cloud. After you have done this sign in. If you have one then simply sign into the Wolfram Cloud.

If you have downloaded the notebook file from the blog and saved it somewhere then do the following to upload it to the cloud so you can play with the code.…

## A Whimsical Introduction to Graph Theory (1)

Part 1 – What are Graphs?

Mathematics is full of fascinating ideas and concepts. These can, however, be very challenging to tackle and make sense of, especially when you are put under pressure to answer questions about them! In this post, and those to come, I hope to share some insight into these concepts without getting too formal. Where some definitions and more technical bits are introduced, they will be explained at end of the post: look out for the dagger $\dagger$ symbols!

To begin, let’s ask what we do in mathematics. The first step in any area of maths is almost always to abstract things. We take some concept we want to be able to work with and pull out the essential ideas. From a bunch of maps we may take out just destinations and the routes between them; from 3D objects we may only need to know what ways we can rotate them and still see the same thing; from a collection of algorithms we may only care about how long they take to run on a computer, and so on.…

## MAM1000W 2017 semester 2, lecture 1 (part ii)

The distance problem

If I want to know how far I walked during an hour, I can ask how far I walked in the first five minutes, and how far I walked in the second five minutes, and how far I walked in the third five minutes, etc. and add them all together. ie. I could write: $d=d_1+d_2+d_3+d_4+...d_{12}$

Where $d_i$ is the distance walked in the $i^{th}$ five minutes. To calculate a distance, we need to know how fast we are going, and for how long. In fact: $distance=velocity \times time$

where you can think of velocity as the same thing as speed (though there are subtle differences which you will find out about later). This formula works if the velocity is constant, but what if it is changing. Well, if we have a graph of velocity against time, then we can think about splitting the graph into intervals (like the five minute intervals above), and approximating that during a small interval of time, the velocity is roughly constant.…