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Statistically Significant Data

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- Biological Resources
- Agriculture Productivity
- Agriculture and Environment
- Agroecosystems
- Antibiotics in Agriculture
- Aquaculture
- Aquatic Food Production
- Biological Pest Control
- Environmental Impact of Agriculture
- Fishing
- Forest Biodiversity
- Genetic Manipulation
- Monoculture
- Pest Control in Agriculture
- Pollinators
- Sustainability in Agriculture
- Technology and Agriculture
- The Biological Productivity Of the Ocean
- Energy Resources
- Domestic Energy
- Electricity Management
- Energy Management Technologies
- Energy Storage Technologies
- Energy Supply and Demand
- Exploitation of Natural Resources
- Fission Energy
- Fusion Research
- Future Energy Resources
- Geothermal Power
- Hydroelectric Power
- New Energy Resources
- New Foods
- Nuclear Power
- Oil and Petroleum
- Renewable Resources
- Resources on Earth
- Solar Power
- Tidal Power
- Transport and Environment
- Water Power
- Wave Energy
- Wind Power
- Environmental Research
- Ecological Study
- Environmental Sampling Methods
- Fieldwork
- Insect Sampling
- Quadrats
- Sample Collection
- Sample Location
- Sample Size
- Sampling Techniques
- Soil Analysis
- Statistically Significant Data
- Trapping
- Living Environment
- Antarctica
- Biodiversity Conservation
- Biodiversity Legislation
- Biogeochemical Cycles
- Biomimetics
- Breeding Success
- CITES
- Conservation Planning
- Deciduous Woodlands
- Ecological Succession
- Ecological Terms
- Environment and Biodiversity
- Ex-situ Conservation
- Habitat Conservation
- Habitat Creation
- Habitat Management
- Human Impact on Biodiversity
- IUCN Red List
- In-Situ Conservation
- Interaction between Environment and Biota
- Interspecies relationships
- Mangroves
- Microclimates
- Oceanic Islands
- Physiological Research
- Population Control
- The Nitrogen Cycle
- Wildlife and Countryside Act 1981
- Physical Environment
- Carbon Footprint
- Climate Change Feedback
- Climate Change Monitoring
- Earth Solar System
- Environmental Impact of Mining
- Geological Processes
- Global Climate Change
- Greenhouse Gases
- Hydrosphere
- Impact of Polluted Water
- Insolation
- Life on Earth
- Lithosphere
- Magnetosphere
- Mineral Reserves
- Mineral Resources
- Mineral Supply
- Montreal Protocol
- Polluted Water
- Rowland-Molina Hypothesis
- Soil Conservation
- Soil Quality
- Soils
- Sustainable Development
- The Atmosphere
- USLE
- UV and IR
- Water Resources Management
- Pollution
- Acid Mines
- Acid Precipitation
- Carbon Monoxide
- Domestic Waste
- Environmental Pollution
- Heavy Metals
- Ionising Radiation
- Lead
- Noise
- Nutrient Pollution
- Oil Pollution
- Oxides of Nitrogen
- PM10
- Pesticide Control
- Pesticides
- Pollutant control
- Pollutants
- Radioactive Waste Management
- Solid Waste
- Thermal Pollution
- Types of Nutrients
- Sustainability

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Jetzt kostenlos anmeldenHave you ever used a statistical test before? It's a type of mathematical test used to analyse data. Using a statistical test helps scientists find out if the data that they have collected proves their theory. Otherwise, it's just a collection of numbers.

Interested in statistically significant data? Read on!

Let's begin with a definition.

**Statistically significant data** refers to results of an experiment that are likely to be attributed to a specific cause.

If data from an experiment is statistically significant, the result is unlikely to have occurred by chance.

It's important to consider sampling error, probability, and certainty.

**Sampling error:**a statistical error that occurs when an analyst selects a sample that does not effectively represent the entire population.**Probability:**the likelihood of an event happening.

**Probability vs Certainty**

Nothing in life is 100% certain. You could grow wings and gain the ability to fly. Yes, it's highly unlikely – but not impossible. There's no way of knowing for sure.

So in statistics, we use probability, not certainty.

What are statistics, and why do we need them?

**Statistics **are tests used to analyse, interpret, and present numerical data.

Without statistical tests, our data doesn't really mean anything. It's just a collection of numbers. But by analysing our data, we can find** associations or differences** between our data, which back up our results and help us to understand the natural environment better.

When planning an experiment, scientists make two hypotheses – the null hypothesis and the alternative hypothesis.

The **null hypothesis (H _{0}) **states that there will be no significant relationships or differences in the data.

The** alternative hypothesis (H _{1})** states that there will be a significant relationship or difference in the data.

Amy wants to know if babies prefer playing with blue toys over orange toys.

H_{0}: Babies do not prefer playing with blue toys over orange toys.

H_{1}: Babies prefer playing with blue toys over orange toys.

If the experiment shows a statistically significant result, the null hypothesis will be **rejected**. If it doesn't show a statistically significant result, the null hypothesis will be **accepted**.

To check that the results of a statistical test are significant, we need to check the significance level and the degrees of freedom.

The **significance level** (also called the p-value) is the probability of incorrectly rejecting the null hypothesis.

The p-value describes probability, not certainty.

In biology and environmental science, the significance level is **0.05**. That means that if there is a** less than 5% chance of incorrectly rejecting the null hypothesis**, the data is considered statistically significant.

Degrees of freedom = **n – 1**, where n is the size of the data set.

Using degrees of freedom helps us find** critical cut-off values** for statistical tests. The more degrees of freedom there are, the larger the critical value.

Critical values are found in tables. You can easily find them online, or in a statistics book.

Variance is a way of measuring the differences between two datasets. It considers **the spread of data points** within a dataset.

Scientists can test for variance using the **F-test**. How does it work?

Calculate the

**mean**of your data set.Subtract each data point from the mean to find its deviation.

**Square**each deviation to ensure you have a positive number.Find the

**sum**of the squares.Divide the squares by

**n-1**to find out the variances.**Divide**the larger variance by the smaller variance to find the calculated F-value.**Compare**the calculated value to the critical value. If the calculated value is less than the critical value, there is a statistically significant variance.

n is the size of your dataset.

A meteorologist wanted to see if there is a significant difference between the wind speed in Hull and the wind speed in Nottingham. She wrote two hypotheses.

**Null Hypothesis:**There is no significant difference between the wind speed in Hull and the wind speed in Nottingham.**Alternative Hypothesis:**There is a significant difference between the wind speed in Hull and the wind speed in Nottingham.

Then, she collected monthly averages and used them to calculate the variance.

Month | Hull: Wind Speed (kph) | Hull: Deviation | Hull: Deviation^{2} | Nottingham: Wind Speed (kph) | Nottingham: Deviation | Nottingham: Deviation^{2} |

January | 24.3 | -4.2 | 17.64 | 21.5 | -3.2 | 10.24 |

February | 23.0 | -2.9 | 8.41 | 20.7 | -2.4 | 5.76 |

March | 21.5 | -1.4 | 1.96 | 19.8 | -1.5 | 2.25 |

April | 18.9 | 1.2 | 1.44 | 17.6 | 0.7 | 0.39 |

May | 17.7 | 2.4 | 5.76 | 16.8 | 1.5 | 2.25 |

June | 16.3 | 3.8 | 14.44 | 15.7 | 2.6 | 6.76 |

July | 16.1 | 4.0 | 16 | 15.7 | 2.6 | 6.76 |

August | 17.1 | 3.0 | 9 | 16.0 | 2.3 | 5.29 |

September | 19.3 | 0.8 | 0.64 | 17.4 | 0.9 | 0.81 |

October | 21.4 | -1.3 | 1.69 | 18.8 | -0.5 | 0.25 |

November | 22.4 | -2.3 | 5.29 | 19.4 | -1.1 | 1.21 |

December | 23.3 | -3.2 | 10.24 | 20.4 | -2.1 | 4.41 |

Mean | 20.1 | N/A | N/A | 18.3 | N/A | N/A |

Sum | N/A | N/A | 92.51 | N/A | N/A | 46.38 |

For Hull, the mean wind speed is 20.1 kph. The sum of squared deviations is 92.51.

Variance:

92.51 ÷ (12-1)

92.51 ÷ 11 = 8.41

For Nottingham, the mean wind speed is 18.3 kph. The sum of squared deviations is 46.38.

Variance:

46.38 ÷ (12-1)

46.38 ÷ 11 = 4.22

Calculated F-value = 8.41 ÷ 4.22 = **1.99**

Finally, the meteorologist found the **critical f-value** from a table. She made sure to check the **degrees of freedom** (in this example, 11) and the **significance level **(0.05).

For this test, the critical F-value is 2.16.

As the calculated F-value is** less than** the critical F-value, **there is a statistically significant variance** between the datasets. The meteorologist** rejected** the null hypothesis.

Spearman's Rank Correlation Coefficient is used to test for an **association or relationship between two variables**. The relationship can be positive or negative.

**Positive relationship:**an increase in one variable is associated with an increase in the other**Negative relationship:**an increase in one variable is associated with a decrease in the other

When performing a Spearman's Rank test, it's important to understand that **correlation ≠ causation**. Just because two things are linked doesn't mean that one causes a change in the other.

Chocolate consumption per capita is correlated with Nobel Prizes per capita. Unfortunately, that doesn't necessarily mean that eating more chocolate makes you smarter!

How does Spearman's Rank work?

**Rank**the data points for both variables.Work out the

**difference**between the ranks.**Square**the difference in ranks to ensure that you have a positive number.**Substitute**your data into the equation shown below to find the calculated r-value.**Compare**the calculated value to the critical value. If the calculated value is equal to or above the critical value, there is a statistically significant variance.

When ranking data, it can be from smallest to largest or largest to smallest. Just make sure that you rank both variables using the same method.

**Equation:** p = 1 – (6 x ∑ D^{2}) ÷ (n(n^{2}-1))

- D: difference in ranks
- n: number of data points in the set

A zoologist wanted to see if the number of spots on a Dalmatian was related to its weight. He wrote two hypotheses.

**Null Hypothesis:**The number of spots on a Dalmatian is not related to its weight.**Alternative Hypothesis:**The number of spots on a Dalmatian is related to its weight.

He weighed ten adult Dalmatians and counted how many spots they had.

Weight (kg) | Spots | Rank of Weight | Rank of Spots | Difference between Ranks | Difference^{2} |

24.8 | 113 | 6 | 5 | 1 | 1 |

22.2 | 144 | 3 | 8 | -5 | 25 |

19.3 | 199 | 1 | 10 | -9 | 81 |

28.9 | 65 | 9 | 2 | 7 | 49 |

26.0 | 129 | 7 | 7 | 0 | 0 |

20.1 | 78 | 2 | 3 | -1 | 1 |

31.2 | 145 | 10 | 9 | 1 | 1 |

23.5 | 50 | 4 | 1 | 3 | 9 |

24.5 | 123 | 5 | 6 | -1 | 1 |

26.7 | 110 | 8 | 4 | 4 | 16 |

Sum | 184 |

Then, the zoologist inserted the data into the equation.

p = 1 – (6 × 184) ÷ (12(12^{2}-1))

p = 1 – (1104 ÷ 1716)

Calculated p-value = **0.356**

Finally, the zoologist found the critical p-value. For this test, the critical p-value was **0.553**. As the calculated p-value was less than the critical p-value, **there is not a statistically significant correlation** between the variables. The zoologist **accepted** the null hypothesis.

I hope that this article has clarified statistically significant data for you. Statistically significant data is a result that is very unlikely to have occurred by chance. To determine if your data is statistically significant, you need to compare your calculated value to the critical value (which is dependent on the significance level and the degrees of freedom).

- Statistically
- We use statistical tests to find associations or differences in our data. This backs up our results and helps us to understand the natural world better.
- When planning an experiment, we write a null hypothesis and an alternative hypothesis. If the result is statistically significant, the null hypothesis is rejected.
- When testing for significance, we need to use significance levels (usually 0.05) and degrees of freedom (n-1).
- Variance measures the difference between two datasets, taking into consideration the spread of data points. An F-test is used to see if variance is statistically significant.
- A correlation tests for an association or relationship between two variables. A Spearman's Rank test is used to see if the correlation is statistically significant.

^{1. Aloys Leo Prinz}^{, Chocolate consumption and Noble laureates, }^{Social Sciences & Humanities Open}^{, 2020}

^{2. Harry Dean, Are Dalmatians Born With Spots: Most Don’t Know This, The Puppy Mag, 2022}

^{3. Hill’s, Dalmatian Dog Breed Information and Personality Traits, 2022}

^{4. Weather Spark, Climate and Average Weather Year Round in Hull, 2022}

^{5. Weather Spark, Climate and Average Weather Year Round in Nottingham, 2022}

Statistically significant data is a result that is very unlikely to have occurred by chance.

Flashcards in Statistically Significant Data15

Start learningDefine statistically significant data.

Statistically significant data refers to results of an experiment that are likely to be attributed to a specific cause.

Why are statistics important?

Statistical tests back up our results and help us to understand the natural environment better.

The** **alternative hypothesis states that there will be a significant relationship or difference in the data.

True

What is the significance level used in environmental science?

p = 0.05

How do you calculate degrees of freedom?

n - 1

What is variance?

Variance is a way of measuring the differences between two datasets. It considers the spread of data points.

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