What determines Sex Ratio in Mammals?

What is the expected sex ratio for just about all organisms? In mammals, who determines the sex of the offspring? Males or females Explain your answer to the previous 2 questions

Male Female XY X XX 50% Male 50% Female X X 2 X X : 2 X Y X X X X X 1

Write down observations about the 2 graphs

Which sex would natural selection favor investment in? Justify your answer to the question above. A justification has 3 components: 1) scientific knowledge and/or theory; 2) specific data from your analysis related to the knowledge; and 3) and explanation of how the data from your analysis supports the knowledge.

Under what conditions would natural selection favor investment in females? Justify your answer to the question above. A justification has 3 components: 1) scientific knowledge and/or theory; 2) specific data from your analysis related to the knowledge; and 3) an explanation of HOW the data from your analysis supports the knowledge.

The p-value is a somewhat arbitrary value used to determine if the results from an experiment are statistically significant. Reject the H0 You would expect results to fall in this area 5% of the time Accept the H0 You would expect results to fall in this area 95% of the time

What is the probability of making a type I error? Reject the H0 You would expect results to fall in this area 5% of the time Accept the H0 You would expect results to fall in this area 95% of the time

Explain how this Chi squared distribution curve illustrates the probability of making a type I error 5% because we would expect these results to happen 5% of the time, just by chance. If these results happened due to chance alone, that means the H0 is true and we rejected a true H0 making a false positive error. In other words, we thought our independent variable was doing something, but it wasn’t. Our HA is not supported.

What is the probability of making a type I error? Reject the H0 You would expect results to fall in this area 20% of the time Accept the H0 You would expect results to fall in this area 80% of the time

Explain how this Chi squared distribution curve illustrates the probability of making a type I error 20% because we would expect these results to happen 20% of the time by chance alone. If these results happened due to chance alone, that means the H0 is true and we rejected a true H0 making a false positive error. In other words, we thought our independent variable was doing something, but it wasn’t. Our HA is not supported.

Why don’t we make a significance level of 1% or less so we stop making type I errors (false positives)? Reject the H0 You would expect results to fall in this area 1% of the time Accept the H0 You would expect results to fall in this area 99% of the time

The lower we set the p-value, the greater the probability of making a type II error or false negative (accepting or failing to reject a false H0) Reject the H0 You would expect results to fall in this area 1% of the time Accept the H0 You would expect results to fall in this area 99% of the time

Unlikely to make a type II error or false negative (accepting or failing to reject a false null), but likely to make a type I error or false positive (rejecting a true null hypothesis). In other words, we are very likely to see the affect of our independent variable and also likely to attribute chance effects to our HA. The right balance between type I and type II errors Unlikely to make a type I error or false positive (rejecting a true null) , but likely to make a type II error (accepting or failing to reject a false null hypothesis). In other words we are likely to think real HA effects are due to chance.

Calculate the chi square statistic for flipping a coin 20 times and getting heads 14 times. This is equivalent to saying you got heads 70% of the time Chi statistic = 3.2

What is the probability of getting 14 out of 20 heads? Chi statistic = 3.2 The probability of getting a Chi squared statistic of 3.2 is greater than 5% (p-value > 5%) p-value = 0.074 = 7.4% > 5% 3.2

p-value increases 3.2 Critical value (significant difference from here down) p-value decreases

State the results in terms of H0 and HA 3.2 The probability of getting 14 heads out of 20 coin flips is more than 5%. This means our H0 is accepted. These results are likely due to chance alone. Our HA is not supported by this test. There is insufficient evidence for shenanigans.

Calculate the chi square statistic for flipping a coin 30 times and getting heads 21 times. This is equivalent to saying you got heads 70% of the time Chi statistic = 4.8

What is the probability of getting 21 out of 30 heads? Chi statistic = 4.8 The probability of getting a Chi squared statistic of 4.8 is less than 5% (p-value < 5%) p-value = 0.028 = 2.8% < 5% 4.8

p-value increases Critical value (significant difference from here down) 4.8 p-value decreases

State the results in terms of H0 and HA The probability of getting 21 heads out of 30 coin flips is less than 5%. This means our H0 is rejected. These results are unlikely due to chance alone. Our HA cannot be ruled out. The reason for the results may be due to shenanigans. 4.8

Crosscutting concepts in science Patterns How was the following demonstrated in this lab? Mathematical representations are needed to identify some patterns. Empirical evidence is needed to identify patterns.

Crosscutting concepts in science Cause and effect: Mechanism and Prediction How was the following demonstrated in this lab? Empirical evidence is required to differentiate between cause and correlation and make claims about specific causes and effects. Changes in systems may have various causes that may not have equal effects.

Crosscutting concepts in science •The significance of a phenomenon is dependent on the scale, proportion, and quantity at which it occurs. Crosscutting concepts in science Scale, Proportion, and Quantity How was the following demonstrated in this lab? The significance of a phenomenon is dependent on the scale, proportion, and quantity at which it occurs. Patterns observable at one scale may not be observable or exist at other scales.

Crosscutting concepts in science Systems and system models How was the following demonstrated in this lab? Models (e.g., physical, mathematical, computer models) can be used to simulate systems and interactions—including energy, matter, and information flows—within and between systems at different scales. Models can be used to predict the behavior of a system, but these predictions have limited precision and reliability due to the assumptions and approximations inherent in models.