Inter-Observer Errors are critical in understanding the data as a whole. It’s to understand the error that comes with the interpretation of two or more individuals making observations of the same issue. This is a sole reason why scientists take multiple data sets in order to weed out these inter-observer errors. It can affect the end result, or calculation resulting in human bias.
They are very similar, as my handedness quotient was 60 which is closer to 100 than to 0 (Ambi) which relates to my limb dominance which is “Extreme Right Handed.”
They measure accurately as I’ve always perceived myself as being extremely right handed.
One of the differences I noticed is the stature difference in males when compared to females. The average stature …show more content…
According to the table, Hispanics on average are only 170.4cm in stature, and this may not be the case if you look at the US as a whole. The sample size in the data is too small to assess any change or impact on growth and development within the US.
Disease and Illness can cause specific decrease in fundamental human growth especially at a young age.
“Children who develop a severe illness in infancy or early childhood are more likely to demonstrate inhibited growth patterns.”
Socioeconomic Status can also affect the resources and medication available to a specific individual. This can affect the rate of growth, both positive and negative.
Stature is also genetic and is dependent on the birth genetic information given from either parent. The factors listed above can only impact that rate, but can’t change the genetic code rather the phenotype that is shown.
According to the data collected the relationship between stature and span does not exist. The Correlation Coefficient (r) can prove this, as .0017 is closer to 0 showing us there is no relationship between these two measurements from the population we collected
They are very similar, as my handedness quotient was 60 which is closer to 100 than to 0 (Ambi) which relates to my limb dominance which is “Extreme Right Handed.”
They measure accurately as I’ve always perceived myself as being extremely right handed.
One of the differences I noticed is the stature difference in males when compared to females. The average stature …show more content…
According to the table, Hispanics on average are only 170.4cm in stature, and this may not be the case if you look at the US as a whole. The sample size in the data is too small to assess any change or impact on growth and development within the US.
Disease and Illness can cause specific decrease in fundamental human growth especially at a young age.
“Children who develop a severe illness in infancy or early childhood are more likely to demonstrate inhibited growth patterns.”
Socioeconomic Status can also affect the resources and medication available to a specific individual. This can affect the rate of growth, both positive and negative.
Stature is also genetic and is dependent on the birth genetic information given from either parent. The factors listed above can only impact that rate, but can’t change the genetic code rather the phenotype that is shown.
According to the data collected the relationship between stature and span does not exist. The Correlation Coefficient (r) can prove this, as .0017 is closer to 0 showing us there is no relationship between these two measurements from the population we collected