Unit 8 – using statistics in science
D4 – Evaluate the validity of the interpretation of the results of the correlation
The results of the experiment satisfy the objective that was expected. However, there are some
underlying answers that give reason for why the crosses aren’t all laying along the trendline.
One of these reasons are that the results were collected from a small group of mixed aged college
students. Therefore 15 crosses on a graph could possibly show a different correlation to a graph full
of 100 crosses. By having more crosses not only does this improve the accuracy and therefore
reliability of the experiment, but it would also improve validity as there are more crosses to go off.
With the group of people being of mixed ages, 16-20, this meant that there were anomalies. Most
adults are fully developed by the age of 18 and height growth is unlikely afterwards, but within the
experiment heights of those under the age of 18 were collected. This removes accuracy and
reliability within the experiment as due to full development not being reached within those people,
the results table and correlation graph made does not show how height and foot size affect one
another. To improve this people over the age of 18 should have only be recorded or to make things
even more accurate, gathering a group of people all one age so there are minimal anomalies.
Another reason for all the crosses not lying along the trendline is that correlation does not imply
causality. Therefore it is wrong to say that height affects foot size, when in fact it could be the other
way round and that foot size could affect height, or there could be a complete different factor
involved that wasn’t measured and can affect either one or both of the areas identified. For
example, height might affect foot size, but height can be affected by a bunch of environmental
factors that in return would not affect foot size e.g. diet. This would then go against the validity and
reliability as someone who has a good diet and eats foods that promote growth might be taller than
their expected foot size. Comparing this to someone who is the opposite and is tall genetically, their
foot size would be the correct size for their height.
To conclude, even though validity was achieved, there are ways to improve it as well as improving
both accuracy and reliability.
…………………………………………………………………………………………………………………………………………………….
Thank you for previewing the example, feel free to buy the singular task, or the Unit 8 bundle xxx
D4 – Evaluate the validity of the interpretation of the results of the correlation
The results of the experiment satisfy the objective that was expected. However, there are some
underlying answers that give reason for why the crosses aren’t all laying along the trendline.
One of these reasons are that the results were collected from a small group of mixed aged college
students. Therefore 15 crosses on a graph could possibly show a different correlation to a graph full
of 100 crosses. By having more crosses not only does this improve the accuracy and therefore
reliability of the experiment, but it would also improve validity as there are more crosses to go off.
With the group of people being of mixed ages, 16-20, this meant that there were anomalies. Most
adults are fully developed by the age of 18 and height growth is unlikely afterwards, but within the
experiment heights of those under the age of 18 were collected. This removes accuracy and
reliability within the experiment as due to full development not being reached within those people,
the results table and correlation graph made does not show how height and foot size affect one
another. To improve this people over the age of 18 should have only be recorded or to make things
even more accurate, gathering a group of people all one age so there are minimal anomalies.
Another reason for all the crosses not lying along the trendline is that correlation does not imply
causality. Therefore it is wrong to say that height affects foot size, when in fact it could be the other
way round and that foot size could affect height, or there could be a complete different factor
involved that wasn’t measured and can affect either one or both of the areas identified. For
example, height might affect foot size, but height can be affected by a bunch of environmental
factors that in return would not affect foot size e.g. diet. This would then go against the validity and
reliability as someone who has a good diet and eats foods that promote growth might be taller than
their expected foot size. Comparing this to someone who is the opposite and is tall genetically, their
foot size would be the correct size for their height.
To conclude, even though validity was achieved, there are ways to improve it as well as improving
both accuracy and reliability.
…………………………………………………………………………………………………………………………………………………….
Thank you for previewing the example, feel free to buy the singular task, or the Unit 8 bundle xxx
